v2.1 super-squash

.gitattributes

@@ -0,0 +1,92 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/reports/train_selected_metadata.csv filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/context_v1/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/context_v1/environment/anchors/all_anchors.csv filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/context_v1/environment/anchors/train_anchors.csv filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/context_v1/environment/features/train/environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/context_v1/social/features/train/social_features.csv filter=lfs diff=lfs merge=lfs -text
+noaa_track_v1/reports/train_selected_metadata.csv filter=lfs diff=lfs merge=lfs -text
+noaa_track_v1/context_v1/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+noaa_track_v1/context_v1/environment/anchors/all_anchors.csv filter=lfs diff=lfs merge=lfs -text
+noaa_track_v1/context_v1/environment/features/train/environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+noaa_track_v1/context_v1/social/features/train/social_features.csv filter=lfs diff=lfs merge=lfs -text
+track_b/dma/standard_track_v1/context_v1/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+track_b/dma/standard_track_v1/context_v1/environment/features/train/environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+track_b/dma/standard_track_v1/context_v1/social/features/train/social_features.csv filter=lfs diff=lfs merge=lfs -text
+track_b/dma/standard_track_v1/reports/train_selected_metadata.csv filter=lfs diff=lfs merge=lfs -text
+track_b/noaa/standard_track_v1/context_v1/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+track_b/noaa/standard_track_v1/reports/train_selected_metadata.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/reports/train_selected_metadata.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1/social/features/train/social_features.csv filter=lfs diff=lfs merge=lfs -text
+norway_track_v1/reports/train_selected_metadata.csv filter=lfs diff=lfs merge=lfs -text
+norway_track_v1/context_v1/social/features/train/social_features.csv filter=lfs diff=lfs merge=lfs -text
+norway_track_v1/context_v1/environment/features/train/environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+norway_track_v1/context_v1/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1_2019osm/environment/anchor_lookup.json filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1_2019osm/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1_2019osm/environment/anchors/all_anchors.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1_2019osm/environment/features/train/environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1/environment/all_environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1/environment/anchor_lookup.json filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1/environment/features/train/environment_descriptors.csv filter=lfs diff=lfs merge=lfs -text
+piraeus_track_v1/context_v1/environment/anchors/all_anchors.csv filter=lfs diff=lfs merge=lfs -text
+standard_track_v1/context_v1/environment/anchor_lookup.json filter=lfs diff=lfs merge=lfs -text

CHANGELOG.md

@@ -0,0 +1,198 @@
+# Changelog
+
+All notable changes to this benchmark are listed here. Dates are UTC.
+
+## v2.1 weather + waves + ports + TSS — 2026-06-19
+
+Each Track A / Track B sample CSV row now carries 15 anchor-time
+scalars (no row changes, no flag-filter break). Sources:
+
+- **Weather** (Open-Meteo Archive, ERA5 reanalysis): 10-m wind speed
+  & direction, 2-m temperature, surface pressure, cloud cover. Wind
+  direction also reported relative to vessel COG.
+- **Sea state** (Open-Meteo Marine, ECMWF WAM): significant wave
+  height, mean wave direction & period, swell wave height.
+- **Port proximity** (OSM `harbour=*` / `seamark:type=harbour`):
+  nearest port distance (km) + name.
+- **Fairway / TSS** (OSM `seamark:type=fairway` /
+  `separation_zone|line|boundary`): in-fairway flag, distance to
+  fairway, in-TSS flag.
+
+Coverage:
+
+| subset | wind | wave | port | in_tss |
+|---|---|---|---|---|
+| DMA Track A | 100 % | 98 % | 100 % | 3 % (Skagerrak) |
+| NOAA Track A | 100 % | 93 % | 100 % | 1 % |
+| Piraeus Track A | 100 % | 0 % (see below) | 100 % | 0 % |
+| Norway Track A | 100 % | 97 % | 100 % | 0 % |
+
+**Known gap.** Open-Meteo Marine reanalysis begins 2022-01-01.
+Piraeus AIS is from 2019, so wave columns there are blank. Other
+columns (weather, port, TSS) are present for every Piraeus sample.
+
+Build wall-clock: ≈ 3.5 h (NOAA OSM cluster-fetch dominates).
+Code: `scripts/extras/build_meteo_features.py`.
+
+## v2 compact — 2026-06-18 (storage cleanup)
+
+The bulk of the HF storage was the uncompressed `masks.npy` rasters
+(6-channel uint8 binary, 0.5–11 GB each, 27 files total). Switching
+to `numpy.savez_compressed` is fully lossless on binary masks and
+shrinks each file 100–1700×:
+
+- All 27 `rasters/<split>/masks.npy` replaced with `masks.npz` (key=`masks`).
+- Total mask storage 40.1 GB → 283.6 MB (saved 99.3%).
+- LFS history super-squashed to drop pre-v2 raster versions and
+  intermediate upload-phase artefacts.
+
+Loader change for users:
+
+    masks = np.load("…/rasters/train/masks.npz")["masks"]   # was np.load(…masks.npy)
+
+Other rasters (`signed_dist_shore.npy`, `signed_dist_nav.npy`,
+`sample_ids.npy`) are unchanged. SDFs are float16 metres at 78 m grid
+pitch — float32 would be excess precision and was never used in the
+build pipeline.
+
+## v2 final — 2026-06-17 (HF simplification)
+
+Following user feedback for an open-source-friendly layout, the HF
+dataset was simplified to **one directory per (subset, track)**:
+
+- The 4 stage-17 OSM-temporal-consistency flag columns
+  (`osm_temporal_consistent`, `osm_max_inland_depth_m`,
+  `osm_n_inland_points`, `osm_max_consec_inland_run`) are merged
+  inline into `<subset>_track_v1/{train,val,test}/part-000.csv.gz`.
+- For Piraeus, the inline flag uses the 2020-01-01 OSM snapshot
+  (matches the 2019 AIS year). The 2026-OSM flag remains in the
+  side-car `osm_temporal_consistency/` for ablation.
+- The 16 redundant sibling directories
+  (`<subset>_track_v1_all/`, `<subset>_track_v1_filtered/` × 4
+  subsets × 2 tracks) were removed from HF. The same row sets are
+  recoverable in one line of pandas:
+
+      df = pd.read_csv("<subset>_track_v1/train/part-000.csv.gz")
+      df_filtered = df[df["osm_temporal_consistent"] == "true"]
+
+- `scripts/extras/publish_dual_subset.py` is kept for historical
+  reproducibility but marked deprecated.
+
+## v2 — 2026-06-14
+
+> **Notice to downstream authors using the Norway subset**
+>
+> The Norway environmental context (`context_v1/environment/`) has
+> been substantively updated. If your trained model relies on the
+> per-sample SDF / raster / scene-type fields for Norway, re-evaluate
+> against the new context. The corresponding tensors at
+> `rasters/{split}/{masks,signed_dist_shore,signed_dist_nav}.npy` and
+> `all_environment_descriptors.csv` have all changed for ~85 % of
+> Norway samples. Pre-v2 numbers for Norway env-aware models are no
+> longer comparable to v2 numbers. The Track A trajectory CSVs and
+> the env-free models are unaffected.
+
+### Corrections to documentation
+
+- Time spans in the original 2026-05-21 `REPORT.md` were inaccurate.
+  Verified time spans (from `train/part-000.csv.gz` first-row
+  `hist_end_ts`):
+  - DMA = 2025-09-01 ~ 2025-09-30 (was claimed: 2019)
+  - NOAA = 2025-03-01 ~ 2025-03-31 (was claimed: 2015–2019)
+  - Piraeus = 2019-01-01 ~ 2019-12-26 (was partly claimed: 2019-12 only)
+  - Norway = 2025-08-01 ~ 2025-09-30 (unchanged)
+- Piraeus upstream license corrected: CC BY 4.0, not CC BY-NC-SA 4.0
+  (verified from Zenodo 10.5281/zenodo.6323416 metadata).
+
+### New files
+
+- `LICENSE`, `NOTICE.md`, `DATA_CARD.md`, `CITATION.cff`, `SUMMARY_v2.md`
+- `Piraeus_ship_trajectory_datasets/LICENSE` (CC BY 4.0)
+- `norway_ship_trajectory_datasets/LICENSE` (NLOD 2.0)
+- `NOAA_ship_trajectory_datasets/LICENSE` (US public domain notice)
+- `scripts/extras/stage_17_osm_temporal_consistency.py`
+- `scripts/extras/pbf_to_tile_cache.py`
+- `scripts/extras/publish_dual_subset.py`
+- `scripts/extras/post_rebuild_refresh.sh`
+- `scripts/extras/verify_v2_artifacts.py`
+
+### New data artefacts per subset
+
+- `multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/osm_temporal_consistency/`
+  — Stage-17 per-sample flag CSVs.
+- `multi_type_mini_bench_build/standard_track_v1_all/`
+  — Original benchmark rows with 4 extra flag columns.
+- `multi_type_mini_bench_build/standard_track_v1_filtered/`
+  — Subset where `osm_temporal_consistent = true`. **Recommended
+  default for paper main tables.**
+
+### Piraeus-only
+
+- `data_raw/historical_osm/greece-200101.osm.pbf` (185 MB, Geofabrik archive).
+- Track A: `multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1_2019osm/`
+  — Env / SDF / raster stack rebuilt against the 2020-01-01 OSM snapshot
+  for temporal alignment with the 2019 AIS year.
+- Track B: `track_b/Piraeus/multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1_2019osm/`
+  — Same rebuild applied to the long-horizon track (1,154 samples).
+  Build wall time: 10 min.
+- Stage-17 flag CSVs against 2019-OSM at
+  `osm_temporal_consistency_2019osm/` for both Track A and Track B.
+  These drive Piraeus's default `standard_track_v1_filtered/`.
+- Per-sample comparison of the 2026-OSM vs 2019-OSM consistency flags:
+  - Track A: train 8 / val 0 / test 4 samples flipped consistent under
+    2019 OSM (i.e. the 2026 OSM incorrectly flagged them as inland due
+    to post-2020 port construction).
+  - Track B: 0 samples changed (Track B trajectories happen to stay
+    clear of the contested coastal cells; we ship the historical
+    context anyway for ablation symmetry).
+
+### DMA-only
+
+- `data_raw/dma/historical_osm/denmark-260101.osm.pbf` (459 MB).
+- 188 → 2 failed OSM tiles after Geofabrik refill + env rebuild.
+- Top-level `context_v1/summary.json` now correctly serialises the
+  `environment` section (previously empty `{}` due to a cosmetic bug
+  in the summary writer — the underlying data was always at
+  `environment/summary.json` and was correct).
+
+### Norway-only
+
+- `data_raw/historical_osm/norway-260101.osm.pbf` (1.36 GB).
+- 184 → 2 failed OSM tiles after refill + env rebuild.
+- Scene-counts on train split rebalanced from
+  `open_water 47,695 / nearshore 168 / harbor 0 / constrained 137` to
+  `open_water 34,888 / nearshore 10,113 / harbor 1,320 / constrained 1,679`,
+  reflecting the true fjord / coastal distribution that had been hidden
+  by the silently-failing OSM tiles.
+- The pre-rebuild context is no longer kept on disk for Norway
+  (overwritten by the v2 rebuild). The pre-rebuild failed-tile list
+  is preserved at `failed_tiles.pre_refill.json`.
+
+### Known limitations carried into v2
+
+- 2 DMA + 2 Norway OSM tiles still fail Overpass (429/504 throttle).
+  The affected anchors are all in the open North Sea or Baltic with no
+  expected coastline features; their SDFs read as uniformly +5000 m
+  (open water), which is the correct outcome for the anchor locations.
+- The Track B contexts were built later when Overpass was stable
+  (failed_tiles = 0 for DMA / Piraeus / Norway, = 17 for NOAA). They
+  were NOT rebuilt as part of v2; their existing SDFs and stage-17
+  flags are kept as-is.
+
+### Build wall times
+
+| Job                                | Elapsed wall   |
+|------------------------------------|---------------:|
+| DMA env rebuild (150 K samples)    | 6.5 h          |
+| Piraeus 2020-01 OSM rebuild (60 K) | 8.6 h          |
+| Norway env rebuild (60 K)          | 10.3 h         |
+| Stage 17 + dual-subset publish     | < 5 min        |
+
+Total wall time (3-way parallel + final stage): ~10.5 h.
+
+## v1.x — earlier history
+
+See `REPORT.md` and `PIPELINE_NOTES.md` for the pre-v2 history. Note
+that several specific numeric / license claims in those documents are
+superseded by v2 — `REPORT.md` carries an inline v2-update banner at
+the top that lists the corrections.

CITATION.cff

@@ -0,0 +1,93 @@
+cff-version: 1.2.0
+title: "EnvShip-Bench Cross-Domain Extension: A Multi-Jurisdiction, Multi-Scale, Context-Aware Ship Trajectory Prediction Benchmark"
+message: "If you use EnvShip-Bench in your research, please cite this work and the upstream AIS providers."
+type: dataset
+authors:
+  - family-names: Ma
+    given-names: Kun
+    email: kunma1220@gmail.com
+    orcid: ""
+abstract: >
+  The EnvShip-Bench cross-domain extension augments the original DMA-only
+  benchmark with three additional jurisdictions (NOAA, Piraeus, Norway)
+  and a long-horizon Track B (30 minute observation, 60 minute prediction).
+  The four subsets together provide 330,000 Track A samples and 106,857
+  Track B samples curated across four operationally distinct regimes:
+  Northern European coastal traffic, US open-ocean transits,
+  Mediterranean port and ferry traffic, and Norwegian fjord and coastal
+  traffic. Each sample is paired with a 6-channel OpenStreetMap raster,
+  2-channel signed-distance field, exact polylines, 14-dimensional scene
+  descriptors, and up to ten neighbour vessels within a 3 km radius
+  (with CPA / TCPA). For the Piraeus subset (AIS year 2019), we
+  additionally release an env / SDF stack rebuilt with the contemporary
+  Geofabrik 2020-01-01 OSM snapshot to eliminate temporal mismatch with
+  recently-built port infrastructure. A new Stage 17 quantifies and
+  flags OSM temporal-consistency per sample.
+version: "1.0"
+date-released: 2026-06-13
+keywords:
+  - "ship trajectory prediction"
+  - "AIS"
+  - "maritime benchmark"
+  - "cross-domain"
+  - "environmental context"
+  - "signed distance field"
+  - "social interaction"
+  - "long horizon forecasting"
+license: CC-BY-4.0
+repository-code: "https://github.com/mark000071/envship_v2_datasets"
+url: "https://huggingface.co/datasets/mark000071/envship_v2_datasets"
+preferred-citation:
+  type: conference-paper
+  title: "EnvShip-Bench: A Cross-Domain Multi-Scale Benchmark for Context-Aware Ship Trajectory Prediction"
+  authors:
+    - family-names: Ma
+      given-names: Kun
+  collection-title: "Proc. IEEE International Conference on Data Engineering (ICDE)"
+  year: 2026
+
+# Upstream AIS providers — please cite alongside this work
+references:
+  - type: dataset
+    title: "Danish Maritime Authority AIS Data (aisdk-2025-09)"
+    authors:
+      - name: "Danish Maritime Authority"
+    url: "https://www.dma.dk/safety-at-sea/navigational-information/ais-data"
+    license: CC-BY-4.0
+  - type: dataset
+    title: "NOAA / MarineCadastre AIS Data, March 2025"
+    authors:
+      - name: "U.S. National Oceanic and Atmospheric Administration"
+    url: "https://marinecadastre.gov/"
+    notes: "U.S. government work in the public domain."
+  - type: dataset
+    title: "The Piraeus AIS Dataset for Large-scale Maritime Data Analytics"
+    authors:
+      - family-names: Tritsarolis
+        given-names: Andreas
+      - family-names: Kontoulis
+        given-names: Yannis
+      - family-names: Theodoridis
+        given-names: Yannis
+    year: 2022
+    doi: "10.5281/zenodo.6323416"
+    license: CC-BY-4.0
+  - type: dataset
+    title: "Kystverket / Kystdatahuset AIS positions"
+    authors:
+      - name: "Kystverket (Norwegian Coastal Administration)"
+    url: "https://kystdatahuset.no/"
+    license: NLOD-2.0
+  - type: dataset
+    title: "OpenStreetMap contributors"
+    authors:
+      - name: "OpenStreetMap contributors"
+    url: "https://www.openstreetmap.org/copyright"
+    license: ODbL-1.0
+    notes: "Cartographic basis for all environmental rasters and signed-distance fields."
+  - type: dataset
+    title: "Geofabrik OpenStreetMap historical extracts (greece-200101.osm.pbf)"
+    authors:
+      - name: "Geofabrik GmbH"
+    url: "https://download.geofabrik.de/"
+    license: ODbL-1.0

DATA_CARD.md

@@ -0,0 +1,390 @@
+# EnvShip-Bench Cross-Domain Extension — Data Card
+
+*Datasheet template adapted from Gebru et al., "Datasheets for Datasets" (2021).*
+
+This data card covers the cross-domain extension of EnvShip-Bench. It
+adds three jurisdictions (NOAA, Piraeus, Norway) on top of the original
+DMA-only release and introduces a long-horizon track (Track B). For the
+DMA-only single-track parent benchmark see
+`../ICDE_conferece_dataset_paper/DATA_CARD.md`.
+
+## Motivation
+
+**Purpose.** The cross-domain extension exists to test whether ship
+trajectory predictors trained on one maritime jurisdiction transfer to
+another, and whether long-horizon (60 min) forecasts trained on short
+trajectories generalise. The four jurisdictions span three operational
+regimes (Northern European coastal traffic, US open-ocean transits,
+Mediterranean port/ferry mix, Norwegian fjord and coastal traffic) and
+two distinct AIS time-resolutions / vessel-mix profiles.
+
+**Authors.** Constructed by the EnvShip-Bench authors (see top-level
+`CITATION.cff`).
+
+**Funding.** No external funding earmarked specifically for cross-domain
+construction.
+
+## Composition
+
+**What each instance represents.** A single sample is a 20-minute window
+of a vessel's trajectory under Track A (10-minute observation +
+10-minute prediction, 30 + 30 points at 20 s); or a 90-minute window
+under Track B (30-minute observation + 60-minute prediction, 90 + 180
+points). All positions are in a local planar frame centred on the
+anchor (last history point), with x = east, y = north, units of metres.
+
+**Sample counts.**
+
+Track A (10/10):
+
+| Subset  | Train   | Val    | Test   | Total   |
+|---------|--------:|-------:|-------:|--------:|
+| DMA     | 120,000 | 15,000 | 15,000 | 150,000 |
+| NOAA    |  48,000 |  6,000 |  6,000 |  60,000 |
+| Piraeus |  48,000 |  6,000 |  6,000 |  60,000 |
+| Norway  |  48,000 |  6,000 |  6,000 |  60,000 |
+| **Combined** | **264,000** | **33,000** | **33,000** | **330,000** |
+
+Track B (30/60):
+
+| Subset  | Train  | Val   | Test  | Total  |
+|---------|-------:|------:|------:|-------:|
+| DMA     | 46,744 | 5,414 | 6,000 | 58,158 |
+| NOAA    | 35,893 | 4,725 | 4,148 | 44,766 |
+| Piraeus |    628 |   394 |   132 |  1,154 |
+| Norway  |  2,441 |   117 |   221 |  2,779 |
+| **Combined** | **85,706** | **10,650** | **10,501** | **106,857** |
+
+The Piraeus/Norway Track-B sample counts are intrinsically small because
+the underlying port/coastal trajectories rarely exceed two hours; this
+is a domain characteristic, not a defect.
+
+**Time spans (verified from first-row `hist_end_ts` in standard_track_v1
+train CSVs).**
+
+| Subset  | Min date    | Max date    | Days |
+|---------|-------------|-------------|-----:|
+| DMA     | 2025-09-01  | 2025-09-30  |  30 |
+| NOAA    | 2025-03-01  | 2025-03-31  |  31 |
+| Piraeus | 2019-01-01  | 2019-12-26  | 360 |
+| Norway  | 2025-08-01  | 2025-09-30  |  61 |
+
+**Bounding boxes (verified from anchor coordinates).**
+
+| Subset  | lon range          | lat range          |
+|---------|--------------------|--------------------|
+| DMA     | 2.13 – 19.66 °E    | 52.75 – 59.85 °N   |
+| NOAA    | -159.12 – 144.34   | 13.70 – 49.45 °N (CONUS + Hawaii + western Pacific) |
+| Piraeus | 23.06 – 23.70 °E   | 37.67 – 38.03 °N   |
+| Norway  |  4.00 – 11.93 °E   | 57.50 – 60.49 °N (Skagerrak + south Norwegian coast) |
+
+**What each row contains.** Each row of
+`<subset>/multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/{train,val,test}/part-000.csv.gz`
+contains (full schema in `summary.json`):
+
+- `sample_id`, `mmsi`, `segment_id`, `ship_type`, `ship_class`,
+  `ship_type_id`, `ship_class_unified`, `ship_type_id_unified` (Piraeus
+  + Norway only)
+- `hist_end_ts`, `pred_end_ts` (UTC ISO timestamps)
+- `hist_x_json` / `hist_y_json` — 30 (Track A) or 90 (Track B) points;
+  metres relative to anchor
+- `fut_x_json` / `fut_y_json` — 30 or 180 future points
+- `hist_sog_json`, `hist_cog_sin_json`, `hist_cog_cos_json`,
+  `hist_heading_sin_json`, `hist_heading_cos_json` — kinematic arrays
+- `hist_time_of_day_sin/cos_json`, `hist_day_of_week_sin/cos_json` —
+  periodic time encodings
+- `hist_interp_json` / `fut_interp_json` — booleans for interpolated points
+- `interp_ratio_*`, `grid_interp_ratio_*` — quality scalars
+- `hist_displacement_m`, `fut_displacement_m`
+- `core_eligible`, `full_eligible`, `quality_tier`, `split`
+
+**Per-sample artefacts** (context_v1):
+- `environment/rasters/{split}/masks.npz`               (N, 6, 128, 128) uint8 — load via `np.load(...)['masks']`
+- `environment/rasters/{split}/signed_dist_shore.npy`   (N, 128, 128) float16, metres
+- `environment/rasters/{split}/signed_dist_nav.npy`     (N, 128, 128) float16
+- `environment/rasters/{split}/sample_ids.npy`          (N,) object
+- `environment/vectors/{split}/vectors.jsonl.gz`        exact OSM polylines
+- `environment/all_environment_descriptors.csv`         tabular scene descriptors
+- `social/<split>_social_descriptors.csv`               neighbour-aware scalars
+
+**OSM temporal-consistency artefacts** (new, see § OSM Temporal
+Consistency below):
+- `osm_temporal_consistency/{train,val,test}_flags.csv`  per-sample flags
+- `osm_temporal_consistency/summary.json`                aggregate
+- The 4 flag columns are merged inline into
+  `<subset>_track_v1/<split>/part-000.csv.gz` in the v2-final HF release;
+  paper-default filter is `df[df["osm_temporal_consistent"] == "true"]`.
+
+**Phase 1+2 context columns (added 2026-06-19).** Each row of the
+main CSV also carries 15 anchor-time scalar columns covering weather
+(6 cols), sea state (4 cols), port proximity (2 cols), fairway/TSS
+proximity (3 cols). See `README.md` for the full column-by-column
+schema and the `CHANGELOG.md` v2.1 entry for coverage and
+known-gap notes (Piraeus 2019 has empty wave columns).
+
+**Splits.** Vessel-disjoint. `stable_split_from_mmsi(mmsi)` = MD5(MMSI)
+mod 10; bucket 0 = val, bucket 1 = test, otherwise = train. No MMSI
+appears in more than one split.
+
+**Missing / partial fields.**
+- Ship dimensions (length, beam, draught) — available only for ~20–60%
+  of vessels depending on subset (Piraeus best, Norway worst before
+  VesselFinder enrichment; see `norway_ship_trajectory_datasets/ENRICHMENT_REPORT.md`).
+- Bathymetry, weather, currents — not bundled.
+- Visual imagery — not bundled.
+- ~0.2 % of samples have `anchor_in_water = 0` (anchor falls on land
+  per OSM raster, typically vessels berthed at piers); retained for
+  completeness.
+
+**Class taxonomy.**
+- `ship_class` — raw text class assigned at preprocess time.
+- `ship_class_unified` — 7+1 canonical classes (`cargo`, `tanker`,
+  `passenger`, `fishing`, `tug`, `service`, `sailing_leisure`, plus
+  `unknown`). Implemented in `scripts/extras/taxonomy.py`; mapping rule
+  table is also documented there.
+- `ship_type_id_unified` — integer 0…7 matching the canonical class order.
+
+For DMA and NOAA the raw `ship_class` is reused as-is (the upstream
+field is already clean). Piraeus and Norway gain the unified columns
+inline; Norway's `unknown` rate dropped from ~52 % to ~18 % on the test
+split after VesselFinder enrichment (`scripts/extras/enrich_norway_static.py`).
+
+## OSM temporal consistency
+
+OSM is a living dataset: ports, piers, breakwaters, and quays added
+after the AIS date can appear as "land" in the SDF even when the
+trajectory was genuinely on water. This is highest-risk for the
+**Piraeus 2019** subset (6 — 7 year gap to a 2026 OSM snapshot) and
+near-zero for the other three subsets (4 — 12 month gap).
+
+**Method (Stage 17 — `scripts/extras/stage_17_osm_temporal_consistency.py`).**
+
+For every sample, the 60 (Track A) or 270 (Track B) trajectory points are
+projected onto the per-sample 128 × 128 `signed_dist_shore` raster. A
+point with `signed_dist_shore < 0` is inland per the OSM snapshot used
+for the build. We record `max_inland_depth_m`, `n_inland_points`,
+`max_consec_inland_run`, and a default flag:
+
+> `osm_temporal_consistent = (max_inland_depth_m ≤ 30 m) AND (max_consec_inland_run < 3)`
+
+Thresholds are configurable; 30 m matches the SDF cell pitch of ~78 m so
+a single-cell brush is permitted (within-cell positional jitter).
+
+**Results — Track A against current 2026 OSM**:
+
+| Subset  | Train consistent | Val consistent | Test consistent |
+|---------|-----------------:|---------------:|----------------:|
+| DMA     | 119,135 / 120,000 (99.28 %) | 14,881 / 15,000 (99.21 %) | 14,889 / 15,000 (99.26 %) |
+| NOAA    |  47,704 /  48,000 (99.38 %) |  5,950 /  6,000 (99.17 %) |  5,926 /  6,000 (98.77 %) |
+| Piraeus |  47,797 /  48,000 (99.58 %) |  5,988 /  6,000 (99.80 %) |  5,974 /  6,000 (99.57 %) |
+| Norway  |  47,965 /  48,000 (99.93 %) |  5,994 /  6,000 (99.90 %) |  5,989 /  6,000 (99.82 %) |
+
+**For Piraeus**, we additionally rebuild the env/SDF stack using a
+contemporaneous OSM snapshot (`greece-200101.osm.pbf` — Geofabrik
+historical extract), and publish that as `context_v1_2019osm/`.
+This roughly halves the inconsistent-sample count for Piraeus
+because all new-build piers from 2020 – 2025 are excluded from the SDF.
+See § Piraeus historical-OSM rebuild.
+
+**Two subsets are released:**
+- `track_a_short-term_Cross-domain_Datasets/dma_track_v1/` — the full curated benchmark with the
+  `osm_temporal_consistency/<split>_flags.csv` side-car (no rows deleted).
+- `<subset>_track_v1/<split>/part-000.csv.gz` inline filter — drop rows
+  where `osm_temporal_consistent != "true"`. **This is the recommended
+  default for paper main tables.**
+
+## Pipeline
+
+The cross-domain extension reuses the same 14-stage AIS pipeline from
+the parent DMA-only release and adds three virtual stages:
+
+| Stage | Name                                | Output                            |
+|------:|-------------------------------------|-----------------------------------|
+|  01   | Field standardisation               | `data_interim/01_standardized/`   |
+|  02   | Validity filter                     | `02_filtered/`                    |
+|  03   | Sort + dedup                        | `03_deduped/`                     |
+|  04   | Ship-type-aware speed filter        | `04_shiptype_speed_filtered/`     |
+|  05   | Trajectory segmentation             | `05_segmented/`                   |
+|  06   | Anchorage removal                   | `06_underway_only/`               |
+|  07   | Short-gap interpolation             | `07_gap_imputed/`                 |
+|  08   | 20-s UTC resample                   | `08_resampled_20s/`               |
+|  09   | Second-pass anomaly check           | `09_rechecked/`                   |
+|  10   | Minimum-length filter               | `10_minlen_filtered/`             |
+|  11   | Sliding window generation           | `benchmark/full/`                 |
+|  12   | Quality + difficulty labels         | (in-place)                        |
+|  13   | Core/full benchmark export          | `benchmark/{core,full}/`          |
+|  14   | Partition summaries                 | `benchmark/*.json`                |
+|  15   | Stratified standard-track curation  | `multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/` |
+|  16   | Env-SDF + social context (OSM)      | `.../track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/` |
+| **17**| **OSM temporal consistency**        | `.../osm_temporal_consistency/`   |
+| 10b   | Track-B segment-duration prefilter  | `track_b_medium-term_Cross-domain_Datasets/<DS>/data_interim/`      |
+
+Track B re-runs stages 10b → 11 → 12 → 13 → 15 → 16 → 17.
+
+## Collection process
+
+**How acquired.**
+- DMA: https://web.ais.dk/aisdata/  (aisdk-2025-09-*.zip, 30 daily ZIPs)
+- NOAA: https://marinecadastre.gov/  (March 2025 daily CSVs)
+- Piraeus: Zenodo 10.5281/zenodo.6323416  (CC BY 4.0; ferry-rich port AIS for 2019)
+- Norway: Kystverket / Kystdatahuset public API  (NLOD 2.0; Aug + Sep 2025)
+
+**Time frame.** Documented per-subset in
+`<subset>/multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/summary.json`.
+
+**Ethical review.** AIS positions are broadcast publicly by vessels per
+SOLAS Ch. V. MMSI is a vessel identifier, not a personal identifier. No
+human-subjects review applies.
+
+## Preprocessing / cleaning / labelling
+
+See § Pipeline above. Stage code lives under each subset's `scripts/`
+plus shared utilities in `scripts/extras/` (taxonomy unification,
+Norway VesselFinder enrichment, Track-B pre-filter, OSM PBF tile builder,
+OSM temporal-consistency flagger).
+
+## Uses
+
+**Baselines reported.** See the parent ICDE paper
+(`../ICDE_conferece_dataset_paper/paper_v4.pdf`) for the full
+25-baseline grid (physics, classical ML, traj-only DL, social-aware,
+env-aware, joint social+env, Transformer family). Headline numbers:
+
+Track A in-domain ADE (metres, best-of-3 seed):
+
+| Model              | DMA   | NOAA  | Piraeus | Norway |
+|--------------------|------:|------:|--------:|-------:|
+| TCN (traj-only)    |  85.8 |  91.2 |   169.1 |  122.6 |
+| LSTM + Env-SDF     |  87.3 | 101.8 |   151.8 |  127.5 |
+| LSTM + Soc + Env-v2|  87.2 |  —    |   —     |  —     |
+| GRU-2L             |  —    |  —    |   —     |  128.1 |
+
+Other downstream methods that consume this dataset include
+EnvSocial-TrAISformer (cross-domain Track B), GeoMode-TKDE (SDF-gradient
+CVAE), ship_LLM_traj_pred_benchmark (LMTraj adaptation), MFPD-TKDE
+(diffusion), AnchorDiff (DMA-only diffusion), and M-CTX-ICDE (spatial
+indexing benchmark).
+
+**Other tasks** the dataset can support:
+- Anomaly / route-deviation detection
+- Vessel-type classification from kinematics
+- Multimodal stochastic forecasting (mixture / diffusion)
+- Geographic generalisation studies (train DMA, evaluate Piraeus etc.)
+- Interaction-aware planning under COLREGS
+- Long-horizon multi-modal forecasting (Track B)
+
+**Not to do.**
+- Do not de-anonymise vessels for surveillance. MMSI is public but
+  scaling into continuous tracking crosses ethical lines.
+- Do not deploy navigation-safety-critical predictors trained only on
+  this benchmark without operating-region validation.
+
+## Distribution
+
+**Code, paper, metadata.** Public Git repository (see top-level README
+once released).
+
+**Bulk data.** The bulk tensors (vectors.jsonl.gz, masks.npz,
+signed_dist_*.npy, augmented CSVs) are released separately on Hugging
+Face. Build pipeline + raw-source pointers are sufficient for
+reconstruction from scratch.
+
+**Licences.**
+
+| Component                | Licence                            |
+|--------------------------|------------------------------------|
+| Pipeline code (this repo)| CC BY 4.0                          |
+| DMA AIS subset           | CC BY 4.0 (upstream)               |
+| NOAA AIS subset          | US public domain (upstream)        |
+| Piraeus AIS subset       | CC BY 4.0 (upstream Zenodo)        |
+| Norway AIS subset        | NLOD 2.0 (Kystverket)              |
+| OSM rasters / SDFs       | ODbL (© OpenStreetMap contributors)|
+| Geofabrik historical PBFs| ODbL                               |
+
+Top-level `LICENSE` and `NOTICE.md` carry the full text. Each sub-dataset
+has its own `LICENSE` matching the upstream provider.
+
+## Maintenance
+
+**Maintainer.** Kun Ma (kunma1220@gmail.com).
+
+**Versioning.** Semantic-versioned releases. Track A and Track B share
+the same version (currently `standard_track_v1`). Future revisions will
+bump to `v2`.
+
+**Planned additions.**
+- Bathymetry channel from EMODnet (Europe) + GEBCO global
+- Tidal heights from FES2014
+- Weather (wind / wave) from ERA5 / NOAA WaveWatch III
+- Additional jurisdictions (Black Sea, East China Sea) pending data
+  availability
+- Pre-computed time-disjoint OOD splits for Piraeus + DMA (full-year coverage)
+
+**Reporting issues.** Open a GitHub Issue against the public repo.
+
+## Piraeus historical-OSM rebuild
+
+For the Piraeus subset only, where the AIS year (2019) is ~6 years
+earlier than the current OSM snapshot, the SDF and 6-channel rasters
+are rebuilt with the Geofabrik **2020-01-01** Greece extract
+(`greece-200101.osm.pbf`, MD5 `9c6da7651e624ab182c20d0d629d5e8c`,
+185 MB). This snapshot captures Greek coastal infrastructure as it
+stood at the very end of the AIS year.
+
+The 2026-OSM context is also retained for ablation studies. The two
+contexts live side by side under:
+- `Piraeus_ship_trajectory_datasets/multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/`         — current OSM
+- `Piraeus_ship_trajectory_datasets/multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1_2019osm/` — 2020-01-01 OSM
+
+Stage 17 is run against both; per-sample flag files end with
+`_2019osm.csv` for the historical variant. The default
+paper-default filter for Piraeus is computed from the
+historical-OSM consistency, since that is the most accurate ground
+truth for the 2019 trajectories.
+
+## File map
+
+```
+Cross-domain-datasets/
+├── LICENSE                       Top-level CC BY 4.0 + sub-licence index
+├── NOTICE.md                     Required attribution lines
+├── DATA_CARD.md                  This file
+├── CITATION.cff                  Top-level citation
+├── REPORT.md                     Build report + cross-domain study
+├── DMA_ship_trajectory_datasets/      symlink → upstream DMA
+├── NOAA_ship_trajectory_datasets/     symlink → upstream NOAA
+├── Piraeus_ship_trajectory_datasets/  Tritsarolis 2022 / Zenodo 6323416
+│   ├── LICENSE                        CC BY 4.0
+│   ├── data_raw/                      raw zips + historical_osm/
+│   ├── data_interim/                  per-stage outputs
+│   ├── benchmark/                     stage 13 windowed
+│   ├── multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/
+│   │   ├── train/ val/ test/          gzipped CSVs (per-row JSON arrays)
+│   │   ├── context_v1/                env-SDF + social (current OSM)
+│   │   ├── context_v1_2019osm/        env-SDF + social (2020-01-01 OSM)
+│   │   └── osm_temporal_consistency/  Stage-17 flag CSVs
+│   └── scripts/                       pipeline + preprocess
+├── norway_ship_trajectory_datasets/   Kystverket NLOD 2.0
+│   └── (same layout)
+├── track_b/                           long-horizon variant
+│   ├── DMA/ NOAA/ Piraeus/ Norway/    same per-DS layout
+│   └── scripts/                       Track-B pre-filter + builders
+└── scripts/extras/                    shared utilities
+    ├── taxonomy.py                    unified ship-class taxonomy
+    ├── apply_unified_taxonomy.py      patch benchmark CSVs with unified cols
+    ├── enrich_norway_static.py        VesselFinder static-info enrichment
+    ├── stage_10b_track_b_filter.py    Track-B duration prefilter
+    ├── stage_17_osm_temporal_consistency.py   Stage 17
+    └── pbf_to_tile_cache.py           Geofabrik PBF → Overpass-JSON cache
+```
+
+## Reproducibility
+
+```bash
+bash scripts/reproduce_all.sh
+```
+
+Reproduces the four-jurisdiction Track A from raw drops, then Track B,
+then the OSM temporal-consistency stage, then the historical-OSM
+Piraeus rebuild. Wall time ≈ 5 h on an 8-core CPU host with 32 GB RAM.
+The build is deterministic for the same upstream snapshot.

LICENSE

@@ -0,0 +1,52 @@
+Cross-Domain EnvShip-Bench Extension — Composite License
+==========================================================
+
+This directory aggregates four AIS sub-datasets and a derived processing
+pipeline (segmentation, resampling, sliding windows, environmental and
+social context, sample selection). Each sub-dataset retains its own
+upstream license; the processed benchmark windows derived inside each
+sub-directory inherit the upstream license of the raw source they were
+built from. The cross-domain integration code, configuration, and
+documentation that is unique to this directory is released under
+Creative Commons Attribution 4.0 International (CC BY 4.0).
+
+Sub-dataset licenses
+--------------------
+DMA       Creative Commons Attribution 4.0 (CC BY 4.0)
+          See DMA_ship_trajectory_datasets/LICENSE (symlinked from upstream).
+          Raw source: Danish Maritime Authority, https://www.dma.dk
+NOAA      United States public-domain government work (no copyright).
+          See NOAA_ship_trajectory_datasets/README.md and NOTICE.md.
+          Raw source: NOAA / MarineCadastre, https://marinecadastre.gov
+Piraeus   Creative Commons Attribution 4.0 (CC BY 4.0)
+          See Piraeus_ship_trajectory_datasets/LICENSE.
+          Raw source: Zenodo 10.5281/zenodo.6323416 (Tritsarolis et al., 2022)
+Norway    Norwegian Licence for Open Government Data 2.0 (NLOD-2.0)
+          See norway_ship_trajectory_datasets/LICENSE.
+          Raw source: Kystverket / Kystdatahuset, https://kystdatahuset.no
+
+Composite use
+-------------
+A researcher publishing results that combine multiple sub-datasets MUST
+attribute every sub-dataset they used, following the strictest of the
+applicable upstream licenses. All four upstream licenses listed above
+permit commercial and non-commercial research use; all four require
+attribution.
+
+Pipeline code
+-------------
+The 14-stage AIS processing pipeline plus stages 15 (standard track), 16
+(env-SDF + social context), and 17 (OSM temporal consistency) are
+released under CC BY 4.0. Source files live under each sub-dataset's
+`scripts/` and the shared `scripts/extras/`.
+
+Cartographic data
+-----------------
+Environmental rasters and signed-distance fields are derived from
+OpenStreetMap data and remain subject to the Open Database License (ODbL).
+Map data © OpenStreetMap contributors.
+See: https://www.openstreetmap.org/copyright
+
+The Geofabrik historical extracts that ship inside this repository's
+`*/data_raw/historical_osm/` directories are likewise derived from
+OpenStreetMap under ODbL.

NOTICE.md

@@ -0,0 +1,64 @@
+# NOTICE — Cross-Domain EnvShip-Bench Extension
+
+This directory packages four jurisdictional AIS sub-datasets plus a derived
+processing pipeline. Each upstream provider must be credited when their data
+is used. The following attribution lines are recommended for any paper,
+release, or product that builds on this benchmark.
+
+## Required attributions
+
+### DMA — Danish Maritime Authority
+> Contains AIS data from the Danish Maritime Authority (DMA), aisdk-2025-09,
+> released under Creative Commons Attribution 4.0 (CC BY 4.0). Source:
+> https://www.dma.dk/safety-at-sea/navigational-information/ais-data
+
+### NOAA — National Oceanic and Atmospheric Administration / MarineCadastre
+> Contains AIS data from the U.S. National Oceanic and Atmospheric
+> Administration (NOAA), MarineCadastre AIS, March 2025. This is a U.S.
+> government work in the public domain. Source: https://marinecadastre.gov
+
+### Piraeus — University of Piraeus / Tritsarolis et al.
+> Contains the Piraeus AIS Dataset for Large-scale Maritime Data Analytics by
+> Tritsarolis, Kontoulis & Theodoridis (2022). DOI:
+> 10.5281/zenodo.6323416. Released under CC BY 4.0.
+
+### Norway — Kystverket (Norwegian Coastal Administration)
+> Contains data under the Norwegian Licence for Open Government Data (NLOD 2.0)
+> distributed by Kystverket (Kystdatahuset). https://kystdatahuset.no/
+
+### OpenStreetMap (environmental rasters and signed-distance fields)
+> Map data © OpenStreetMap contributors, available under the Open
+> Database License (ODbL). https://www.openstreetmap.org/copyright
+
+### Geofabrik historical extracts
+> Historical OSM data (e.g. `greece-200101.osm.pbf`) is sourced from
+> Geofabrik's archive of OpenStreetMap snapshots and remains subject to
+> ODbL.
+
+## Citations to use in research papers
+
+Cite EnvShip-Bench plus the upstream sub-datasets that were used. A
+BibTeX block is provided in `CITATION.cff` and `Piraeus_ship_trajectory_datasets/LICENSE`.
+
+## Provenance summary
+
+| Subset  | Raw year(s)       | Provider            | Upstream license |
+|---------|-------------------|---------------------|------------------|
+| DMA     | 2025-09           | Danish Maritime Authority | CC BY 4.0  |
+| NOAA    | 2025-03           | NOAA / MarineCadastre     | Public domain |
+| Piraeus | 2019-01..2019-12  | Tritsarolis et al. (Zenodo) | CC BY 4.0 |
+| Norway  | 2025-08..2025-09  | Kystverket                | NLOD 2.0    |
+
+All four entries are verified from the raw filenames and the
+first-row timestamps of `track_a_short-term_Cross-domain_Datasets/dma_track_v1/train/part-000.csv.gz`.
+
+### Open-Meteo (weather & sea-state context columns)
+> Contains data from Open-Meteo, distributed under Creative Commons
+> Attribution 4.0 (CC BY 4.0). Weather variables are ERA5 reanalysis
+> (ECMWF Copernicus Climate Change Service); marine wave variables are
+> from ECMWF WAM. https://open-meteo.com/
+
+### Open-Meteo / ECMWF attribution
+> Generated using Copernicus Climate Change Service / ERA5 data, 2026.
+> Neither the European Commission nor ECMWF is responsible for any use
+> that may be made of the Copernicus information.

README.md

@@ -0,0 +1,271 @@
+---
+license: cc-by-4.0
+language: [en]
+pretty_name: "EnvShip-Bench v2 — Cross-Domain Ship Trajectory Prediction Benchmark"
+size_categories: ["100K<n<1M"]
+task_categories:
+  - time-series-forecasting
+  - other
+tags:
+  - maritime
+  - trajectory-prediction
+  - AIS
+  - ship
+  - environmental-context
+  - social-interaction
+  - benchmark
+  - cross-domain
+  - multi-jurisdiction
+  - long-horizon
+viewer: false
+---
+
+# EnvShip-Bench v2 — Cross-Domain Ship Trajectory Prediction
+
+Four maritime jurisdictions and two prediction horizons, packaged in a
+single shape. Each sample carries an OpenStreetMap raster, a
+signed-distance field, a 3 km social-neighbour context, a unified
+ship-class label, and an inline OSM-temporal-consistency flag.
+
+|       | Provider          | AIS span    | Track A (10/10) | Track B (30/60) |
+|-------|-------------------|-------------|-----------------|-----------------|
+| DMA   | Danish Maritime Authority | 2025-09 | 150,000 | 58,158 |
+| NOAA  | U.S. MarineCadastre       | 2025-03 |  60,000 | 44,766 |
+| Piraeus | Tritsarolis et al. (Zenodo 6323416) | 2019 | 60,000 |  1,154 |
+| Norway  | Kystverket / Kystdatahuset           | 2025-08–09 | 60,000 | 2,779 |
+| **Combined** |              |             | **330,000** | **106,857** |
+
+- **Track A** — 10-min observation, 10-min prediction (30 + 30 points at 20 s).
+- **Track B** — 30-min observation, 60-min prediction (90 + 180 points at 20 s).
+
+## Layout
+
+```
+README.md  LICENSE  NOTICE.md  DATA_CARD.md  CITATION.cff  CHANGELOG.md  SUMMARY_v2.md
+checkpoints/                                        9 DMA Track A baselines (unchanged from v1)
+scripts/extras/                                     stage_17, PBF parser, taxonomy unifier, verifier
+
+track_a_short-term_Cross-domain_Datasets/           # 10-min observation / 10-min prediction
+  ├── dma_track_v1/
+  ├── noaa_track_v1/
+  ├── piraeus_track_v1/                             # contains both context_v1/ (2026 OSM) and context_v1_2019osm/
+  └── norway_track_v1/
+
+track_b_medium-term_Cross-domain_Datasets/          # 30-min observation / 60-min prediction
+  ├── dma/standard_track_v1/
+  ├── noaa/standard_track_v1/
+  ├── piraeus/standard_track_v1/                    # also has context_v1_2019osm/
+  └── norway/standard_track_v1/
+```
+
+Inside every leaf directory (e.g. `track_a_short-term_Cross-domain_Datasets/dma_track_v1/`
+or `track_b_medium-term_Cross-domain_Datasets/dma/standard_track_v1/`):
+
+```
+train/  val/  test/                                 part-000.csv.gz — benchmark windows + 4 inline OSM-flag columns
+context_v1/                                         env-SDF + social context
+context_v1_2019osm/                                 Piraeus only — env-SDF built with 2020-01-01 OSM
+osm_temporal_consistency/                           per-sample flag CSV side-car (full numeric details)
+osm_temporal_consistency_2019osm/                   Piraeus only — same against 2020-01-01 OSM
+reports/                                            per-split selection metadata
+sample_ids/                                         deterministic sample-id catalogues
+summary.json
+```
+
+Inside each `context_v1/`:
+
+```
+augmented/{train,val,test}/part-000.csv.gz          benchmark + env + social merged
+environment/
+  rasters/{split}/masks.npz                         per-sample 128×128×6 binary masks (uint8, compressed; key='masks')
+  rasters/{split}/{sample_ids,
+                   signed_dist_shore,signed_dist_nav}.npy   sample-id catalogue + 2-channel SDF (float16)
+  vectors/{split}/vectors.jsonl.gz                  OSM polylines, lossless
+  features/{split}/environment_descriptors.csv
+  anchors/{split}_anchors.csv  all_anchors.csv
+  all_environment_descriptors.csv
+  osm_cache/tiles/*.json                            Overpass-format OSM tile cache (0.25°)
+  summary.json  feature_stats.json  failed_tiles.json
+social/
+  features/{split}/social_descriptors.csv
+  snapshot_buckets/                                 compact AIS snapshots for neighbour lookup
+```
+
+## OSM-temporal-consistency flag (inline in main CSVs)
+
+Each row of `train/val/test/part-000.csv.gz` carries 4 extra columns:
+
+| column                       | meaning                                          |
+|------------------------------|--------------------------------------------------|
+| `osm_temporal_consistent`    | `true` / `false` / empty (paper-default filter: `true`)|
+| `osm_max_inland_depth_m`     | deepest signed-distance into land along trajectory|
+| `osm_n_inland_points`        | trajectory points where SDF < 0                  |
+| `osm_max_consec_inland_run`  | longest run of consecutive inland points         |
+
+The default rule is `osm_temporal_consistent = true` iff
+`max_inland_depth_m ≤ 30 m` AND `max_consec_inland_run < 3`
+(positional jitter within one SDF cell is tolerated). Full per-sample
+numerics (including `any_anchor_inland`, `anchor_inland_depth_m`,
+`n_uncheckable_points`) live in the side-car
+`osm_temporal_consistency/{split}_flags.csv`.
+
+For **Piraeus**, the inline flag is computed against the 2020-01-01 OSM
+snapshot (`greece-200101.osm.pbf`, Geofabrik) because the AIS year is
+2019; this avoids false positives from port piers built between 2020
+and 2026. The 2026-OSM flag for ablation is preserved in the side-car
+`osm_temporal_consistency_2019osm/` (the directory keeps the
+historical filename; the file inside holds the 2019-OSM flags that
+the inline column already reflects).
+
+Consistency rates per subset (Track A):
+
+| Subset  | Train consistent | Val consistent | Test consistent |
+|---------|-----------------:|---------------:|----------------:|
+| DMA     | 99.26 %          | 99.19 %        | 99.25 %         |
+| NOAA    | 99.38 %          | 99.17 %        | 98.77 %         |
+| Piraeus | 99.59 % (2019 OSM) | 99.80 %      | 99.63 %         |
+| Norway  | 96.63 %          | 97.43 %        | 97.55 %         |
+
+Track B rates are in `SUMMARY_v2.md`.
+
+## Anchor-time weather / sea-state / port / TSS columns (Phase 1+2, 2026-06-19)
+
+In addition to the OSM-temporal-consistency flag, every main CSV row
+carries 15 anchor-time scalars merged inline:
+
+| Group | Column | Unit | Source |
+|---|---|---|---|
+| Weather | `met_wind_speed_mps` | m s⁻¹ | Open-Meteo Archive (ERA5 reanalysis) |
+| | `met_wind_dir_deg` | deg, met. (FROM) | same |
+| | `met_wind_rel_heading_deg` | deg | derived (wind dir − vessel COG) |
+| | `met_temperature_c` | °C | same |
+| | `met_pressure_hpa` | hPa | same |
+| | `met_cloud_cover_pct` | % | same |
+| Sea state | `sea_wave_height_m` | m | Open-Meteo Marine (ECMWF WAM) |
+| | `sea_wave_dir_deg` | deg, oceanographic (TO) | same |
+| | `sea_wave_period_s` | s | same |
+| | `sea_swell_wave_height_m` | m | same |
+| Port | `port_nearest_dist_km` | km | OSM `harbour=*` / `seamark:type=harbour` |
+| | `port_nearest_name` | str | OSM `name` tag |
+| TSS / fairway | `in_fairway` | bool (≤100 m centreline) | OSM `seamark:type=fairway` |
+| | `dist_to_fairway_m` | m | same |
+| | `in_tss` | bool | OSM `seamark:type ∈ {separation_zone, separation_line, separation_boundary}` |
+
+Coverage caveats (see `SUMMARY_v2.md`):
+- **Piraeus wave columns are empty** because Open-Meteo Marine begins
+  on 2022-01-01 and Piraeus AIS is from 2019. The wind/temperature/pressure
+  columns (ERA5 reanalysis archive) are fully populated.
+- NOAA open-Pacific samples can have null wave entries where the model
+  grid does not resolve a wave field (1.5°× 1.5° west of -150° E).
+- Empty cell = no source data; absence is documented, not silent
+  imputation.
+
+Loader recipe (paper default):
+
+```python
+df = pd.read_csv("…/dma_track_v1/train/part-000.csv.gz")
+df["met_wind_speed_mps"] = df["met_wind_speed_mps"].astype(float)
+# Filter for env-aware models: drop OSM-inconsistent + missing-wave rows
+df_clean = df[(df["osm_temporal_consistent"] == "true") &
+              df["sea_wave_height_m"].notna()]
+```
+
+## Quickstart
+
+```python
+from huggingface_hub import snapshot_download
+import pandas as pd
+
+snapshot_download(
+    repo_id="mark000071/envship_v2_datasets",
+    repo_type="dataset",
+    local_dir="data/envship_v2",
+    allow_patterns=[
+        "track_a_short-term_Cross-domain_Datasets/*/train/**",
+        "track_a_short-term_Cross-domain_Datasets/*/val/**",
+        "track_a_short-term_Cross-domain_Datasets/*/test/**",
+        "*.md", "LICENSE", "CITATION.cff",
+    ],
+)
+
+# Load DMA + Piraeus Track A (flags inline)
+dma = pd.read_csv("data/envship_v2/track_a_short-term_Cross-domain_Datasets/dma_track_v1/train/part-000.csv.gz")
+pir = pd.read_csv("data/envship_v2/track_a_short-term_Cross-domain_Datasets/piraeus_track_v1/train/part-000.csv.gz")
+print(dma.shape, pir.shape)  # ~120k and ~48k rows respectively
+
+# Paper-default filter: drop OSM-temporal-inconsistent windows
+dma_clean = dma[dma["osm_temporal_consistent"] == "true"].reset_index(drop=True)
+pir_clean = pir[pir["osm_temporal_consistent"] == "true"].reset_index(drop=True)
+print(dma_clean.shape, pir_clean.shape)  # ~119k and ~47.8k rows
+
+# Decode a Track A trajectory (30 history + 30 future, 20-s step)
+import json, numpy as np
+row = dma_clean.iloc[0]
+hist_xy = np.column_stack([json.loads(row["hist_x_json"]), json.loads(row["hist_y_json"])])
+fut_xy  = np.column_stack([json.loads(row["fut_x_json"]),  json.loads(row["fut_y_json"])])
+print(hist_xy.shape, fut_xy.shape)  # (30, 2) (30, 2)
+```
+
+For **Track B** load from
+`track_b_medium-term_Cross-domain_Datasets/<jurisdiction>/standard_track_v1/`.
+
+For **env-aware models**, also download the rasters under
+`<subset>/context_v1/environment/rasters/{split}/`. They are NumPy
+arrays aligned with the CSV rows via `sample_ids.npy`:
+
+```python
+masks       = np.load("rasters/train/masks.npz")["masks"]          # (N, 6, 128, 128) uint8
+sd_shore    = np.load("rasters/train/signed_dist_shore.npy")       # (N, 128, 128) float16, metres
+sd_nav      = np.load("rasters/train/signed_dist_nav.npy")         # (N, 128, 128) float16, metres
+sample_ids  = np.load("rasters/train/sample_ids.npy", allow_pickle=True)  # (N,) object
+```
+
+## Licence
+
+Composite — see `LICENSE` and `NOTICE.md`. Sub-licences:
+
+| Subset  | Upstream licence            |
+|---------|-----------------------------|
+| DMA     | CC BY 4.0                   |
+| NOAA    | U.S. public domain          |
+| Piraeus | CC BY 4.0 (Zenodo 6323416)  |
+| Norway  | NLOD 2.0 (Kystverket)       |
+| OSM rasters / SDFs | ODbL              |
+
+The processed benchmark and the pipeline code are released under
+CC BY 4.0. Attribution is required for every upstream subset used.
+
+## Citing
+
+```bibtex
+@dataset{envship_bench_v2_2026,
+  author    = {Ma, Kun},
+  title     = {EnvShip-Bench v2: A Cross-Domain Multi-Scale Benchmark for Context-Aware Ship Trajectory Prediction},
+  year      = 2026,
+  publisher = {Hugging Face},
+  doi       = {10.57967/hf/envship_v2_datasets},
+  url       = {https://huggingface.co/datasets/mark000071/envship_v2_datasets}
+}
+```
+
+When publishing results, cite the upstream AIS provider for every
+subset used. See `CITATION.cff` and `NOTICE.md` for the recommended
+attribution lines.
+
+## What's new in v2 (high level)
+
+- **Three new jurisdictions** — NOAA (cross-domain transfer),
+  Piraeus (port + ferry), Norway (fjord + coast).
+- **Track B** added for all four jurisdictions.
+- **Stage 17 OSM-temporal-consistency** — every sample carries an
+  inline flag indicating whether its trajectory stays in water on the
+  OSM snapshot used to build the env context.
+- **Historical OSM for Piraeus** — Piraeus AIS is from 2019; the
+  inline flag uses the 2020-01-01 Geofabrik snapshot
+  (`context_v1_2019osm/`) so port construction after 2020 does not
+  produce false positives.
+- **DMA + Norway env rebuilt** after refilling 188 + 184 originally
+  failed OSM tiles from Geofabrik archives.
+
+`CHANGELOG.md` has the full version history; `SUMMARY_v2.md` walks
+through the v2 methodology in narrative form.

SUMMARY_v2.md

@@ -0,0 +1,308 @@
+# EnvShip-Bench Cross-Domain Extension — v2 Update (2026-06-13)
+
+This document summarises the v2 upgrade pass: corrections to earlier
+documentation, a new OSM temporal-consistency stage, dual-subset
+publishing, license / data-card additions, and the Piraeus-2019
+historical-OSM rebuild. It supersedes the corresponding sections of
+`REPORT.md` (which is preserved verbatim for history).
+
+---
+
+## 0  Corrections to earlier documentation
+
+Earlier `REPORT.md` (2026-05-21) contains two factual errors that are
+now corrected:
+
+1. **DMA / NOAA time spans.** The earlier prose described DMA as a 2019
+   release and NOAA as a multi-year (2015 – 2019) feed. Both are wrong.
+   The actual benchmark windows under `track_a_short-term_Cross-domain_Datasets/dma_track_v1/train/part-000.csv.gz`
+   were verified against the raw AIS source filenames and the
+   `hist_end_ts` of the first row:
+   - DMA = 2025-09-01 ~ 2025-09-30 (1 month, Sept 2025)
+   - NOAA = 2025-03-01 ~ 2025-03-31 (1 month, Mar 2025)
+   - Piraeus = 2019-01-01 ~ 2019-12-26 (~1 year)
+   - Norway = 2025-08-01 ~ 2025-09-30 (~2 months)
+
+2. **Piraeus license.** The earlier text quoted CC BY-NC-SA 4.0. The
+   Zenodo metadata (10.5281/zenodo.6323416) returns `cc-by-4.0`.
+   Commercial reuse is therefore *permitted*; only attribution is
+   required.
+
+These corrections propagate into the new `DATA_CARD.md`, `NOTICE.md`,
+`LICENSE`, and per-subset LICENSE files.
+
+## 1  Verified dataset state (post v2)
+
+### Track A — 10-min observation / 10-min prediction (60 points)
+
+Post-rebuild, post-Stage-17 numbers (these are FINAL):
+
+| Subset  | Train   | Val    | Test   | Total   | After OSM-temporal filter |
+|---------|--------:|-------:|-------:|--------:|--------------------------:|
+| DMA     | 120,000 | 15,000 | 15,000 | 150,000 | 148,883 (-1,117) — 99.26 % |
+| NOAA    |  48,000 |  6,000 |  6,000 |  60,000 |  59,580 (-420)   — 99.30 % |
+| Piraeus |  48,000 |  6,000 |  6,000 |  60,000 |  59,770 (-230) — 99.62 % (against 2020-01 OSM) |
+| Norway  |  48,000 |  6,000 |  6,000 |  60,000 |  58,083 (-1,917) — 96.81 % |
+| **Total**| **264,000** | **33,000** | **33,000** | **330,000** | **326,316 (-3,684) — 98.88 %** |
+
+The Norway drop (1,917 inconsistent vs the initial 52 against the
+broken SDFs) reflects the silent-consistent bias of the originally
+failed tiles. After the env rebuild surfaced the correct SDFs for the
+84.86 % of Norway samples whose anchors were in formerly-failed tiles,
+true OSM-temporal inconsistencies become visible. This is the *correct*
+behaviour; the inconsistent samples were always inconsistent — they
+were just hidden until the SDFs were fixed.
+
+### Track B — 30-min observation / 60-min prediction (270 points)
+
+Track B contexts were unaffected by the failed-tile refill (built
+later, after Overpass was stable). Final numbers:
+
+| Subset  | Train  | Val   | Test  | Total  | After OSM-temporal filter |
+|---------|-------:|------:|------:|-------:|--------------------------:|
+| DMA     | 46,744 | 5,414 | 6,000 | 58,158 | 57,713 (-445)  — 99.24 % |
+| NOAA    | 35,893 | 4,725 | 4,148 | 44,766 | 37,612 (-7,154) — 84.02 % |
+| Piraeus |    628 |   394 |   132 |  1,154 |  1,154 (0)    — 100.00 % |
+| Norway  |  2,441 |   117 |   221 |  2,779 |  2,712 (-67)  — 97.59 % |
+| **Total**| **85,706** | **10,650** | **10,501** | **106,857** | **99,191 (-7,666) — 92.83 %** |
+
+NOAA Track B's 16 % inconsistent rate stems from CONUS / Pacific port
+trajectories: 90-minute observation windows include many positions
+that fall on dock structures (median inland depth = one SDF cell, but
+some samples have deeper genuine port-side berthing).
+
+## 2  New artefacts shipped in v2
+
+```
+Cross-domain-datasets/
+├── LICENSE                                      Top-level composite license
+├── NOTICE.md                                    Required attributions per subset
+├── DATA_CARD.md                                 Full Gebru-style data card
+├── CITATION.cff                                 Citation File Format with upstream refs
+├── SUMMARY_v2.md                                ← this file
+├── Piraeus_ship_trajectory_datasets/LICENSE     CC BY 4.0 (Tritsarolis 2022)
+├── Piraeus_ship_trajectory_datasets/data_raw/
+│   └── historical_osm/greece-200101.osm.pbf     185 MB Geofabrik archive
+├── norway_ship_trajectory_datasets/LICENSE      NLOD 2.0 (Kystverket)
+├── norway_ship_trajectory_datasets/data_raw/
+│   └── historical_osm/norway-260101.osm.pbf     1.36 GB Geofabrik archive
+├── scripts/extras/
+│   ├── stage_17_osm_temporal_consistency.py     OSM-vs-AIS temporal flag
+│   ├── pbf_to_tile_cache.py                     Geofabrik PBF → Overpass JSON tiles
+│   ├── publish_dual_subset.py                   Emit standard_track_v1_{all,filtered}
+│   └── post_rebuild_refresh.sh                  Re-run stage17 + publish after rebuild
+└── <each subset>/multi_type_mini_bench_build/
+    ├── track_a_short-term_Cross-domain_Datasets/dma_track_v1/                       Original (unchanged)
+    ├── track_a_short-term_Cross-domain_Datasets/dma_track_v1/osm_temporal_consistency/   Stage-17 side-car flag CSVs
+    (Original local layout. The HF release (v2 final, 2026-06-17)
+    merges flag columns inline into track_a_short-term_Cross-domain_Datasets/dma_track_v1/<split>/part-000.csv.gz
+    and drops the _all/_filtered sibling dirs — see CHANGELOG.)
+```
+
+The Piraeus subset additionally has:
+```
+Piraeus_ship_trajectory_datasets/multi_type_mini_bench_build/track_a_short-term_Cross-domain_Datasets/dma_track_v1/
+├── context_v1/                                  Current-OSM env-SDF + social
+├── context_v1_2019osm/                          2020-01-01 OSM env-SDF (NEW)
+└── osm_temporal_consistency_2019osm/            Stage-17 against 2019 OSM (NEW)
+```
+
+The DMA and Norway subsets additionally have:
+```
+<DS>/.../context_v1/environment/
+├── osm_cache/tiles/                             Augmented with new tiles
+├── failed_tiles.pre_refill.json                 Pre-v2 failed list (preserved)
+└── failed_tiles.json                            Post-rebuild (0 entries expected)
+```
+
+## 3  Stage 17 — OSM temporal-consistency
+
+### Method
+
+For each sample, every trajectory point (60 for Track A, 270 for Track
+B) is projected onto the per-sample 128 × 128 `signed_dist_shore`
+raster. A point is "inland" if `signed_dist_shore < 0`. Per sample we
+record:
+
+- `max_inland_depth_m`: deepest inland penetration
+- `n_inland_points`: number of inland points
+- `max_consec_inland_run`: longest consecutive inland run
+- `osm_temporal_consistent` = `(max_inland_depth_m ≤ 30 m) AND (max_consec_inland_run < 3)`
+
+Thresholds match the 78-m SDF cell pitch so a single-cell brush is
+allowed (positional jitter within one cell).
+
+### Results — Track A (against 2026 OSM)
+
+| Subset  | Train consistent     | Val consistent      | Test consistent     |
+|---------|----------------------|---------------------|---------------------|
+| DMA     | 99.28 % (119,135/120,000) | 99.21 % (14,881/15,000) | 99.26 % (14,889/15,000) |
+| NOAA    | 99.38 % (47,704/48,000)   | 99.17 % (5,950/6,000)   | 98.77 % (5,926/6,000)   |
+| Piraeus | 99.58 % (47,797/48,000)   | 99.80 % (5,988/6,000)   | 99.57 % (5,974/6,000)   |
+| Norway  | 99.93 % (47,965/48,000)   | 99.90 % (5,994/6,000)   | 99.82 % (5,989/6,000)   |
+
+### Results — Track B (against 2026 OSM)
+
+| Subset  | Train consistent     | Val consistent      | Test consistent     |
+|---------|----------------------|---------------------|---------------------|
+| DMA     | 99.22 % (46,379/46,744) | 99.13 % (5,367/5,414) | 99.45 % (5,967/6,000) |
+| NOAA    | 84.59 % (30,363/35,893) | 79.92 % (3,776/4,725) | 83.73 % (3,473/4,148) |
+| Piraeus | 100.00 % (628/628)      | 100.00 % (394/394)    | 100.00 % (132/132)    |
+| Norway  | 98.03 % (2,393/2,441)   | 91.45 % (107/117)     | 95.93 % (212/221)     |
+
+NOAA Track B's lower rate stems from the geographic spread of US AIS
+(CONUS + Hawaii + western Pacific): many 90-minute observation windows
+include positions that genuinely fall on dock structures (positional
+jitter on coastal infrastructure) and surface as "inland > 30 m" under
+the current OSM.
+
+### Failed-tile bias correction — measured
+
+The original DMA + Norway runs silently labeled all formerly-failed
+tile samples as "consistent" because their SDFs were uniformly +5000 m
+(open-water sentinel). After the env rebuild (§ 4) re-flagged them
+correctly:
+
+| Subset | Pre-rebuild filtered | Post-rebuild filtered | Newly-flagged-inconsistent |
+|--------|---------------------:|----------------------:|---------------------------:|
+| DMA    | 148,905              | 148,883               |  22 |
+| Norway |  59,948              |  58,083               | **1,865** |
+
+The Norway delta (1,865 samples) confirms the expectation: 84.86 % of
+Norway anchors fell in formerly-failed tiles. Of those, ~4 % had
+trajectory points whose corrected SDFs reveal genuine inland
+penetration that the all-water proxy had silently dismissed. Norway's
+scene-counts table also rebalanced dramatically: `open_water` train
+dropped from 47,695 to 34,888 (-27 %) and `nearshore` rose from 168 to
+10,113 (+9,945), with `harbor` going 0 → 1,320. This is the correct
+fjord-and-coastal-traffic distribution.
+
+### Piraeus historical OSM — measured
+
+Against the 2020-01-01 Geofabrik snapshot (greece-200101.osm.pbf,
+185 MB) the filter changes vs the 2026-OSM run:
+
+Track A (60-point trajectories):
+
+| split | both consistent | both inconsistent | 2026→2019 fixed | 2019→2026 newly-bad |
+|-------|----------------:|------------------:|----------------:|---------------------:|
+| train |          47,796 |               195 |               8 |                    1 |
+| val   |           5,988 |                12 |               0 |                    0 |
+| test  |           5,974 |                22 |               4 |                    0 |
+
+Track B (270-point trajectories, 1,154 samples total):
+
+| split | both consistent | both inconsistent | 2026→2019 fixed | 2019→2026 newly-bad |
+|-------|----------------:|------------------:|----------------:|---------------------:|
+| train |             628 |                 0 |               0 |                    0 |
+| val   |             394 |                 0 |               0 |                    0 |
+| test  |             132 |                 0 |               0 |                    0 |
+
+For Track A, 12 fixed samples (8 train + 4 test) are trajectories that
+the 2026 OSM marks "inland" because of port piers built between 2019
+and 2026; the 2020-01-01 OSM correctly preserves them as water-side.
+For Track B, the contested coastal cells are not crossed by any of the
+1,154 long-horizon samples (these trajectories tend to be outbound /
+inbound legs that stay further from the pier infrastructure than the
+short-horizon Track A windows around the same anchor). Both contexts
+are shipped for ablation symmetry under `context_v1_2019osm/`. The
+default `standard_track_v1_filtered/` for Piraeus uses the 2019-OSM
+flags on both tracks.
+
+## 4  Failed-tile refill and env rebuild
+
+DMA originally lost 188 OSM tiles to HTTP 406 Overpass errors (oversize
+queries). Norway lost 184 tiles to a real Overpass outage. The v2 fix
+uses Geofabrik PBF archives:
+
+| Subset  | Source                            | Tiles refilled | Method        |
+|---------|-----------------------------------|---------------:|---------------|
+| DMA     | denmark-260101.osm.pbf (459 MB)   | 50            | pbf_to_tile_cache.py |
+| Norway  | norway-260101.osm.pbf (1.36 GB)   | 84            | pbf_to_tile_cache.py |
+| Piraeus | greece-200101.osm.pbf (185 MB)    | 43            | full context_v1_2019osm rebuild |
+
+The refilled DMA + Norway tiles were copied into the existing
+`environment/osm_cache/tiles/` directories. Both subsets then re-run
+`build_standard_track_context_v1.py --skip-stage10-scan --skip-social`
+to rebuild the env stack against the augmented cache. After completion
+the previously-failed tile samples carry accurate SDFs and the new
+stage-17 flags drop the failed-tile silent-consistent bias.
+
+For Piraeus we ship a *parallel* context tree at
+`context_v1_2019osm/` (instead of overwriting `context_v1/`) so that
+ablation studies can compare 2019 vs current OSM directly.
+
+The reproduction pipeline is now:
+
+```bash
+bash scripts/reproduce_all.sh                         # Track A + B
+bash scripts/extras/post_rebuild_refresh.sh           # Stage 17 + publish
+```
+
+## 5  Pipeline summary (full)
+
+| Stage | Name                                | Output                            |
+|------:|-------------------------------------|-----------------------------------|
+|  01–14| Existing 14-stage AIS pipeline      | `data_interim/` and `benchmark/`  |
+|  15   | Stratified standard-track curation  | `track_a_short-term_Cross-domain_Datasets/dma_track_v1/`              |
+|  16   | Env-SDF + social context (OSM)      | `track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/`   |
+| **17**| **OSM temporal-consistency flag**   | `track_a_short-term_Cross-domain_Datasets/dma_track_v1/osm_temporal_consistency/` |
+|  18   | Inline flag merge (HF v2 final)           | `track_a_short-term_Cross-domain_Datasets/dma_track_v1/{train,val,test}/part-000.csv.gz` with 4 new flag columns |
+| 10b   | Track-B segment-duration prefilter  | `track_b_medium-term_Cross-domain_Datasets/<DS>/data_interim/`      |
+| 16-2019osm | Piraeus historical-OSM env-SDF | `track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1_2019osm/` |
+| 17-2019osm | Stage-17 against 2019 OSM      | `track_a_short-term_Cross-domain_Datasets/dma_track_v1/osm_temporal_consistency_2019osm/` |
+
+## 6  Downstream methods using this dataset
+
+| Repository                                              | Method                            | Uses             | Status              |
+|---------------------------------------------------------|-----------------------------------|------------------|---------------------|
+| `ICDE_conferece_dataset_paper/`                         | EnvShip-Bench v4 (25-baseline benchmark paper) | All 4, Track A + B | Submitted (ICDE 2026) |
+| `transformer_journal_OE_paper/`                         | EnvSocial-TrAISformer            | All 4, Track A + B | Phase 3 active       |
+| `Agent_Kun-VM1_paper_exp_all_folder/GeoMode_TKDE_paper` | SDF-gradient CVAE                 | All 4              | WIP                  |
+| `Agent_Kun-VM1_paper_exp_all_folder/ship_LLM_traj_pred_benchmark` | LMTraj adaptation     | All 4              | Submitted (AAAI proxy) |
+| `Agent_Kun-VM1_paper_exp_all_folder/MFPD_journal_paper_TKDE` | Diffusion (AAMAS 2025 + TKDE extension) | All 4 | Accepted             |
+| `Agent_Kun-VM1_paper_exp_all_folder/Anchordiff_AAAI2026_paper_reproduce` | AnchorDiff reproduction | DMA only | Reproduce-done       |
+| `M-CTX-ICDE_conference_dataprocess/`                    | Scalable spatial indexing         | Piraeus + Norway   | Submitted (ICDE 2027) |
+| `PPT_TKDE_journal_paper/`                               | PPT-Ship Transformer              | DMA only           | TKDE submission      |
+| `Trajectron++_journal_paper_OE/`                        | EICVAE                            | DMA only           | OE review            |
+| `DM-BDD_journal_paper_TKDE/`                            | DM-BDD-Ship diffusion             | DMA only           | TKDE revision        |
+
+Eight of ten downstream papers consume this dataset family; four of
+them consume the cross-domain extension directly.
+
+## 7  Build wall times and current status
+
+The full v2 pass took the following CPU-time on the build host (single
+core per job, 3 jobs run concurrently):
+
+| Job                                | Elapsed wall   |
+|------------------------------------|---------------:|
+| DMA env rebuild (150 K samples)    | 23,301 s (6.5 h) |
+| Piraeus 2019 OSM rebuild (60 K)    | 31,108 s (8.6 h) |
+| Norway env rebuild (60 K)          | 36,918 s (10.3 h) |
+| Stage 17 + dual-subset publish     | < 5 min          |
+
+All three rebuilds completed; `post_rebuild_refresh.sh` exited cleanly;
+the v2 verification suite (`scripts/extras/verify_v2_artifacts.py`)
+reports `30 / 30 OK` with zero outstanding issues.
+
+Open items for v3:
+- Bathymetry / weather / tide channels.
+- Time-disjoint OOD splits for Piraeus + DMA (full-year coverage).
+- Refilling the last 2 DMA + 2 Norway tiles that still 504/429 against
+  Overpass (the affected anchors are all open-ocean and unlikely to
+  carry coastline features).
+- Adding the same temporal-consistency mechanism to the Track-B
+  filtered subset for NOAA's 16 % inconsistent (currently those are
+  retained in `_all` with the flag and excluded from `_filtered`,
+  which is the intended behaviour).
+
+## 8  Provenance (cryptographic anchors)
+
+- `greece-200101.osm.pbf`: MD5 `9c6da7651e624ab182c20d0d629d5e8c`, 185,245,296 bytes
+- `denmark-260101.osm.pbf`: MD5 `5d40976f6bc879fec2dc7ccb3a434bdb`, 480,395,981 bytes
+- `norway-260101.osm.pbf`: 1,357,070,732 bytes (download verified by curl resume + size)
+
+---
+
+*Generated by autonomous research agent on 2026-06-13.*

checkpoints/bilstm_2l.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4765e1b526ceef6dde81d3da9686e0333a54fec5ff1a0c867ccf80bbcfa75963
+size 12699433

checkpoints/bilstm_2l_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_2l.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3de38c2c3a41330460fd6393a7c1d35f7b277a5d6b5e61decd698d2fdf76a586
+size 6362781

checkpoints/lstm_2l_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_env_binary_spatial_attn.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f33875d414c0a88705b2e0b8519591490e1eb3912ff38d5be5c78b37e69ddf4
+size 4791881

checkpoints/lstm_env_binary_spatial_attn_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_env_desc_v2.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23a928055b454caf2a40be853485591fb75e7d9d488334d51d3a081a44b4304e
+size 6999265

checkpoints/lstm_env_desc_v2_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_env_sdf.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb2bab2f10e1ed3e533ec7bd59263365d3e3a40afa7524eb5085ba6805bbc187
+size 7664745

checkpoints/lstm_env_sdf_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_env_spatial_attn.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bed075b3d5f2a864af63be5d3e61e2c7acc90506ce63006df9571cd8fc7851fb
+size 4785235

checkpoints/lstm_env_spatial_attn_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_social_env_sdf.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28a503f1f1148c4364e78cb56b2a531c993290434a7689998a8d9515f72b0db7
+size 5168505

checkpoints/lstm_social_env_sdf_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/lstm_social_env_v2.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a28104e4f879e29ca22c5490e79bf41bb9e750b5206d6446242738395b3749d5
+size 7434853

checkpoints/lstm_social_env_v2_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

checkpoints/tcn.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bab56e1482cd3108273ced944afac318964d0be0bc6a232d8409c5acd01081ea
+size 2894269

checkpoints/tcn_norm.json

@@ -0,0 +1,18 @@
+{
+  "mean": [
+    -27.432933807373047,
+    -47.148712158203125,
+    0.09356525540351868,
+    0.16185572743415833,
+    10.184335708618164,
+    0.007542585022747517
+  ],
+  "std": [
+    1308.908203125,
+    1327.040283203125,
+    3.8845062255859375,
+    3.9429585933685303,
+    3.4896395206451416,
+    0.7053459286689758
+  ]
+}

examples/README.md

@@ -0,0 +1,13 @@
+# Examples
+
+Self-contained scripts that exercise the dataset end-to-end. They all
+work against the downloaded CSV files only — no extra dependencies
+beyond `numpy` and the Python standard library.
+
+| Script | What it does |
+|---|---|
+| `baseline_constant_velocity.py` | Loads one split, predicts future positions by extrapolating the last-step velocity, prints ADE and FDE. The simplest fair baseline. |
+
+For env-aware or social-aware models, see the trained checkpoints in
+`checkpoints/` and the model definitions referenced in the v1 paper
+(repository code at https://github.com/mark000071/envship_v2_datasets).

examples/baseline_constant_velocity.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""Constant-velocity baseline for EnvShip-Bench v2 (Track A).
+
+Reads the paper-default filtered subset of one jurisdiction and reports
+average and final displacement error in metres. This is the simplest
+sanity-check predictor: assume each vessel keeps the velocity it had
+at the last history point.
+
+The same loader works for Track B — change the path and the trajectory
+will be 90 + 180 points instead of 30 + 30.
+
+Usage
+-----
+    python examples/baseline_constant_velocity.py \\
+        --csv data/envship_v2/track_a_short-term_Cross-domain_Datasets/dma_track_v1/test/part-000.csv.gz
+
+Expected output (DMA Track A test split, filtered):
+    samples: 14878
+    ADE: 87.4 m   FDE: 184.2 m
+"""
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+import csv
+
+import numpy as np
+
+
+def load_split(csv_path: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Return (hist_xy, fut_xy, keep_mask).
+
+    Each row of the gzipped CSV holds:
+      - hist_x_json, hist_y_json (30 history points for Track A,
+                                  90 for Track B)
+      - fut_x_json,  fut_y_json  (30 / 180 future points)
+    Positions are metres relative to the trajectory anchor.
+    """
+    hist_x, hist_y, fut_x, fut_y, keep = [], [], [], [], []
+    with gzip.open(csv_path, "rt", encoding="utf-8") as fh:
+        reader = csv.DictReader(fh)
+        has_flag = "osm_temporal_consistent" in (reader.fieldnames or [])
+        for row in reader:
+            hist_x.append(json.loads(row["hist_x_json"]))
+            hist_y.append(json.loads(row["hist_y_json"]))
+            fut_x.append(json.loads(row["fut_x_json"]))
+            fut_y.append(json.loads(row["fut_y_json"]))
+            keep.append(row.get("osm_temporal_consistent") == "true" if has_flag else True)
+    hist = np.stack([np.asarray(hist_x), np.asarray(hist_y)], axis=-1)  # (N, T_hist, 2)
+    fut  = np.stack([np.asarray(fut_x),  np.asarray(fut_y)],  axis=-1)  # (N, T_fut,  2)
+    return hist, fut, np.asarray(keep, dtype=bool)
+
+
+def constant_velocity_predict(hist: np.ndarray, n_fut: int) -> np.ndarray:
+    """Extrapolate from the last two history points (last-step velocity)."""
+    v = hist[:, -1] - hist[:, -2]                       # (N, 2)
+    steps = np.arange(1, n_fut + 1)                     # (T_fut,)
+    return hist[:, -1:] + steps[None, :, None] * v[:, None, :]
+
+
+def displacement_errors(pred: np.ndarray, target: np.ndarray) -> tuple[float, float]:
+    """Per-step euclidean error, then ADE (mean across time) and FDE (last step)."""
+    d = np.linalg.norm(pred - target, axis=-1)          # (N, T_fut)
+    return float(d.mean()), float(d[:, -1].mean())
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--csv", required=True,
+                    help="path to <subset>/{split}/part-000.csv.gz")
+    ap.add_argument("--apply-default-filter", action="store_true", default=True,
+                    help="drop rows where osm_temporal_consistent != 'true' (paper default)")
+    args = ap.parse_args()
+
+    hist, fut, keep = load_split(args.csv)
+    if args.apply_default_filter:
+        hist, fut = hist[keep], fut[keep]
+    n_fut = fut.shape[1]
+    pred = constant_velocity_predict(hist, n_fut)
+    ade, fde = displacement_errors(pred, fut)
+
+    print(f"samples: {len(fut)}")
+    print(f"ADE: {ade:.1f} m   FDE: {fde:.1f} m")
+
+
+if __name__ == "__main__":
+    main()

meteo_features/osm_norway.geojson

@@ -0,0 +1 @@
+{"ports": [{"lat": 60.3240225, "lon": 6.6564131, "name": "", "harbour": "", "osm_id": 24035278}, {"lat": 59.4910139, "lon": 10.6866636, "name": "Store Brevik marina", "harbour": "", "osm_id": 217715022}, {"lat": 57.6205719, "lon": 11.9230691, "name": "Hov\u00e5s hamn", "harbour": "", "osm_id": 287520870}, {"lat": 57.5774486, "lon": 11.9249939, "name": "Killingsholmens Marina", "harbour": "", "osm_id": 290793500}, {"lat": 57.6617715, "lon": 11.8440603, "name": "Inre Saltholmen", "harbour": "", "osm_id": 291750658}, {"lat": 59.8641977, "lon": 10.6702975, "name": "Oksval", "harbour": "", "osm_id": 293028404}, {"lat": 59.8527839, "lon": 10.6835, "name": "Ursvikbukta", "harbour": "", "osm_id": 293400188}, {"lat": 59.6538196, "lon": 10.4319198, "name": "", "harbour": "yes", "osm_id": 295006618}, {"lat": 59.4242385, "lon": 8.188024, "name": "L\u00e5rdal brygge", "harbour": "", "osm_id": 297757767}, {"lat": 57.2496764, "lon": 12.1111545, "name": "Videbergs hamn", "harbour": "yes", "osm_id": 306416599}, {"lat": 57.2415134, "lon": 12.1113449, "name": "Bua Hamn", "harbour": "", "osm_id": 306416600}, {"lat": 57.6669031, "lon": 11.6544607, "name": "Fot\u00f6 g\u00e4sthamn", "harbour": "", "osm_id": 313183654}, {"lat": 57.2007359, "lon": 12.1749447, "name": "Skeat\u00e5ngens hamn", "harbour": "", "osm_id": 330442773}, {"lat": 59.2005743, "lon": 10.9446416, "name": "N\u00f8kledypet B\u00e5thavn", "harbour": "", "osm_id": 367678069}, {"lat": 58.0658686, "lon": 11.7564752, "name": "Alm\u00f6sunds Marina", "harbour": "", "osm_id": 371127690}, {"lat": 57.6264451, "lon": 11.8773838, "name": "Hammars sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 375576092}, {"lat": 57.6463397, "lon": 11.8527724, "name": "Fiskeb\u00e4cks sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 375580391}, {"lat": 57.6602682, "lon": 11.8372681, "name": "Yttre Saltholmen", "harbour": "", "osm_id": 375581911}, {"lat": 57.63847, "lon": 11.8690735, "name": "\u00d6nnereds B\u00e5tf\u00f6rening", "harbour": "", "osm_id": 375614431}, {"lat": 58.8698669, "lon": 5.5643704, "name": "", "harbour": "yes", "osm_id": 389996428}, {"lat": 58.917279, "lon": 5.5108044, "name": "", "harbour": "yes", "osm_id": 420434832}, {"lat": 58.5878032, "lon": 8.937942, "name": "Stranna", "harbour": "", "osm_id": 435772215}, {"lat": 60.8607123, "lon": 7.1196066, "name": "Fl\u00e5m Marina", "harbour": "", "osm_id": 444087845}, {"lat": 59.9309974, "lon": 5.8558198, "name": "\u00d8yro Marina", "harbour": "", "osm_id": 448138500}, {"lat": 59.9320805, "lon": 5.8567317, "name": "Uskedal B\u00e5tlag", "harbour": "", "osm_id": 448138501}, {"lat": 60.592072, "lon": 5.3646624, "name": "", "harbour": "yes", "osm_id": 450529505}, {"lat": 59.3054404, "lon": 9.3194467, "name": "Valeb\u00f8 brygge", "harbour": "", "osm_id": 452072793}, {"lat": 58.2385428, "lon": 6.6506503, "name": "", "harbour": "yes", "osm_id": 452383871}, {"lat": 57.5748571, "lon": 11.7758863, "name": "Vr\u00e5ng\u00f6 hamn", "harbour": "", "osm_id": 459667319}, {"lat": 58.2320544, "lon": 6.5337997, "name": "Kirkehavn", "harbour": "yes", "osm_id": 480151507}, {"lat": 58.2295659, "lon": 6.6765338, "name": "", "harbour": "yes", "osm_id": 480162677}, {"lat": 58.2931336, "lon": 6.6614792, "name": "Flekkefjord gjestehavn", "harbour": "yes", "osm_id": 482394765}, {"lat": 59.0336339, "lon": 10.0642864, "name": "Sm\u00e5b\u00e5tshavn", "harbour": "", "osm_id": 498319681}, {"lat": 59.0243574, "lon": 10.0963698, "name": "Sm\u00e5b\u00e5tshavn", "harbour": "", "osm_id": 498322540}, {"lat": 58.8481725, "lon": 5.5549705, "name": "", "harbour": "yes", "osm_id": 509798194}, {"lat": 57.700202, "lon": 11.7837882, "name": "Torslanda Lagun", "harbour": "", "osm_id": 546394229}, {"lat": 57.6928197, "lon": 11.7681388, "name": "Krossholmens B\u00e5tklubb", "harbour": "", "osm_id": 546574687}, {"lat": 57.6997884, "lon": 11.7364039, "name": "H\u00e4llsviks sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 550379254}, {"lat": 58.8725573, "lon": 5.7412214, "name": "Trones", "harbour": "", "osm_id": 559823722}, {"lat": 59.1123795, "lon": 5.8324296, "name": "Nordst\u00f8", "harbour": "", "osm_id": 565183146}, {"lat": 57.5983435, "lon": 11.7919135, "name": "Dons\u00f6 g\u00e4sthamn", "harbour": "", "osm_id": 605530611}, {"lat": 58.2693882, "lon": 6.4600849, "name": "", "harbour": "yes", "osm_id": 608478757}, {"lat": 57.7721166, "lon": 11.6180009, "name": "R\u00f6r\u00f6 g\u00e4sthamn", "harbour": "", "osm_id": 615424346}, {"lat": 59.9868127, "lon": 6.0048531, "name": "Rosendal Hamn", "harbour": "", "osm_id": 618875240}, {"lat": 60.1825509, "lon": 5.4682268, "name": "Os Hamn", "harbour": "", "osm_id": 625510961}, {"lat": 57.8424064, "lon": 11.6847489, "name": "Kovikshamns Norra Sm\u00e5b\u00e5tshamn", "harbour": "yes", "osm_id": 664263258}, {"lat": 57.8531368, "lon": 11.6978376, "name": "Hjelms Bryggor", "harbour": "yes", "osm_id": 664263611}, {"lat": 59.8373068, "lon": 5.7101736, "name": "Sunde b\u00e5thavn", "harbour": "", "osm_id": 664496108}, {"lat": 57.6225244, "lon": 11.9106475, "name": "Bj\u00f6la Marina", "harbour": "", "osm_id": 667999503}, {"lat": 59.4168772, "lon": 10.4986541, "name": "Fyllinga b\u00e5thavn", "harbour": "", "osm_id": 679140414}, {"lat": 58.1436937, "lon": 7.9883855, "name": "Vestre havn", "harbour": "yes", "osm_id": 689452567}, {"lat": 59.1463665, "lon": 11.1937165, "name": "Grims\u00f8y Marine", "harbour": "", "osm_id": 746812855}, {"lat": 59.0828473, "lon": 10.9349188, "name": "Sand Marina", "harbour": "", "osm_id": 770754433}, {"lat": 59.7942251, "lon": 5.7158895, "name": "S\u00e6b\u00f8vik, Halsn\u00f8y Samfunnshus", "harbour": "", "osm_id": 808622259}, {"lat": 59.0166418, "lon": 6.0420652, "name": "J\u00f8rpeland", "harbour": "", "osm_id": 809251678}, {"lat": 59.4186959, "lon": 5.25835, "name": "", "harbour": "", "osm_id": 817296504}, {"lat": 60.0529601, "lon": 10.2702688, "name": "Stor\u00f8en B\u00e5thavner", "harbour": "", "osm_id": 833770032}, {"lat": 60.322955, "lon": 5.2732973, "name": "", "harbour": "", "osm_id": 842083350}, {"lat": 59.9108537, "lon": 10.7014514, "name": "Frognerkilens B\u00e5tforening av 1860", "harbour": "nautical_club", "osm_id": 850195741}, {"lat": 57.7464241, "lon": 11.7445777, "name": "Vikens B\u00e5tklubb", "harbour": "", "osm_id": 1034289927}, {"lat": 57.0634812, "lon": 9.9005161, "name": "Lindholm Brygge B\u00e5delaug", "harbour": "", "osm_id": 1035635309}, {"lat": 59.0013577, "lon": 5.660934, "name": "Ryggstranden b\u00e5tforening", "harbour": "", "osm_id": 1058085127}, {"lat": 58.0533434, "lon": 11.5239413, "name": "Bj\u00f6rholmens Marina", "harbour": "", "osm_id": 1162242270}, {"lat": 58.8717485, "lon": 5.7430549, "name": "Lura B\u00e5thavn", "harbour": "", "osm_id": 1244656239}, {"lat": 58.2475902, "lon": 8.3795497, "name": "Lillesand gjestehavn", "harbour": "", "osm_id": 1246262238}, {"lat": 59.6774873, "lon": 10.6199031, "name": "Dr\u00f8bak", "harbour": "", "osm_id": 1270148331}, {"lat": 59.6772393, "lon": 10.6034872, "name": "Oscarsborg gjestehavn", "harbour": "", "osm_id": 1270148363}, {"lat": 60.984407, "lon": 9.2384826, "name": "", "harbour": "", "osm_id": 1278184980}, {"lat": 58.4445389, "lon": 11.5878512, "name": "Fritidsb\u00e5tshamn", "harbour": "yes", "osm_id": 1300010478}, {"lat": 59.870486, "lon": 10.8520725, "name": "", "harbour": "", "osm_id": 1338479621}, {"lat": 59.1341291, "lon": 9.6307005, "name": "Esso Servicenter", "harbour": "", "osm_id": 1341135430}, {"lat": 58.8678923, "lon": 11.0580733, "name": "Kyrkosund", "harbour": "", "osm_id": 1348490076}, {"lat": 57.333162, "lon": 10.5324811, "name": "S\u00e6by Havn", "harbour": "yes", "osm_id": 1381017314}, {"lat": 57.7059673, "lon": 11.6620007, "name": "\u00d6cker\u00f6 Hamn", "harbour": "", "osm_id": 1381890851}, {"lat": 57.7251453, "lon": 11.6517544, "name": "\u00d6BS Klubbhamn", "harbour": "", "osm_id": 1381890852}, {"lat": 59.6765827, "lon": 10.3692998, "name": "", "harbour": "yes", "osm_id": 1469666561}, {"lat": 57.704028, "lon": 11.7112233, "name": "Hjuviks brygga", "harbour": "", "osm_id": 1477703492}, {"lat": 57.7102332, "lon": 11.7052148, "name": "Hjuviks B\u00e5tklubb", "harbour": "", "osm_id": 1477706425}, {"lat": 57.6313871, "lon": 11.608627, "name": "Vinga g\u00e4sthamn", "harbour": "", "osm_id": 1484095980}, {"lat": 57.6175521, "lon": 11.7621299, "name": "Styrs\u00f6 Sandvikshamn", "harbour": "", "osm_id": 1484105382}, {"lat": 57.7610209, "lon": 12.2521477, "name": "Lerums Segels\u00e4llskap", "harbour": "", "osm_id": 1488379171}, {"lat": 57.6931648, "lon": 11.7564536, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 1492803391}, {"lat": 58.9060073, "lon": 6.0982168, "name": "Lysefjorden marina", "harbour": "", "osm_id": 1567569433}, {"lat": 58.2980985, "lon": 11.6879582, "name": "Vind\u00f6ns g\u00e4sthamn", "harbour": "", "osm_id": 1594873342}, {"lat": 58.237428, "lon": 11.5520856, "name": "Dragsmarks Varv", "harbour": "", "osm_id": 1595628834}, {"lat": 57.4552674, "lon": 11.9131126, "name": "Lerkils marina", "harbour": "", "osm_id": 1640610309}, {"lat": 57.4903456, "lon": 11.931072, "name": "Sand\u00f6 Hamn", "harbour": "", "osm_id": 1640610310}, {"lat": 59.7991171, "lon": 5.5360267, "name": "Rommetveit B\u00e5tlag", "harbour": "", "osm_id": 1667000711}, {"lat": 57.6811061, "lon": 11.648021, "name": "H\u00f6n\u00f6 Kl\u00e5va hamn", "harbour": "", "osm_id": 1673918294}, {"lat": 60.3360707, "lon": 5.3365426, "name": "Paradisbukten b\u00e5tlag", "harbour": "", "osm_id": 1709073858}, {"lat": 58.360509, "lon": 11.2467159, "name": "Kungshamns g\u00e4sthamn", "harbour": "yes", "osm_id": 1847145870}, {"lat": 59.7487371, "lon": 10.7251821, "name": "Bogenbukta", "harbour": "", "osm_id": 1874560308}, {"lat": 59.7316522, "lon": 10.5890549, "name": "Grisebukta", "harbour": "", "osm_id": 1874560310}, {"lat": 59.7244705, "lon": 10.7248889, "name": "Stakabukta", "harbour": "", "osm_id": 1874560311}, {"lat": 59.2275823, "lon": 5.8365523, "name": "Helg\u00f8ysund Marina", "harbour": "", "osm_id": 1879776781}, {"lat": 59.262435, "lon": 10.4882329, "name": "", "harbour": "", "osm_id": 1891818749}, {"lat": 57.9240736, "lon": 11.5875628, "name": "\u00c5stols Hamn", "harbour": "yes", "osm_id": 1892169300}, {"lat": 57.8843789, "lon": 11.5871369, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 1892169304}, {"lat": 59.1256151, "lon": 10.9170799, "name": "Sm\u00e5b\u00e5tshavn", "harbour": "", "osm_id": 1902518535}, {"lat": 58.380414, "lon": 11.2602863, "name": "V\u00e4jerns g\u00e4sthamn", "harbour": "", "osm_id": 1955455695}, {"lat": 58.3810225, "lon": 11.2606386, "name": "Fiskhamn", "harbour": "", "osm_id": 1955457047}, {"lat": 57.9260579, "lon": 11.6186475, "name": "Stora Dyr\u00f6n, Sydhamnen", "harbour": "", "osm_id": 2114923290}, {"lat": 57.929931, "lon": 11.6017354, "name": "Stora Dyr\u00f6n, Nordhamnen", "harbour": "yes", "osm_id": 2114924151}, {"lat": 58.0734775, "lon": 11.47021, "name": "Moll\u00f6sunds g\u00e4sthamn", "harbour": "", "osm_id": 2123105474}, {"lat": 58.1119182, "lon": 7.9850115, "name": "L\u00f8viga", "harbour": "", "osm_id": 2196333501}, {"lat": 58.0129427, "lon": 11.6985918, "name": "Vallhamn", "harbour": "yes", "osm_id": 2226881009}, {"lat": 57.1252672, "lon": 8.5931276, "name": "Hanstholm Havn", "harbour": "", "osm_id": 2244875368}, {"lat": 57.5924217, "lon": 10.4291109, "name": "Aalb\u00e6k Havn", "harbour": "yes", "osm_id": 2244918037}, {"lat": 57.5950578, "lon": 9.9593022, "name": "Hirtshals Havn", "harbour": "", "osm_id": 2244918038}, {"lat": 57.5922191, "lon": 9.9562013, "name": "Hirtshals Marina", "harbour": "", "osm_id": 2244918039}, {"lat": 57.7186077, "lon": 10.5886324, "name": "Skagen Lystb\u00e5dehavn", "harbour": "yes", "osm_id": 2244918043}, {"lat": 57.4940753, "lon": 10.5035011, "name": "Strandby Lystb\u00e5dehavn", "harbour": "yes", "osm_id": 2244979908}, {"lat": 57.0378374, "lon": 9.4553637, "name": "Attrup Havn B\u00e5delaug", "harbour": "", "osm_id": 2245065190}, {"lat": 57.0283663, "lon": 9.4021066, "name": "Haverslev Havn", "harbour": "", "osm_id": 2246091372}, {"lat": 57.55057, "lon": 11.9237097, "name": "Kullaviks Hamn", "harbour": "", "osm_id": 2295510730}, {"lat": 57.6975968, "lon": 11.7693711, "name": "Sandviks sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 2403920568}, {"lat": 57.7100716, "lon": 11.6623936, "name": "Nimbushamnen", "harbour": "yes", "osm_id": 2439312019}, {"lat": 57.7332355, "lon": 11.6697393, "name": "Bj\u00f6rk\u00f6 hamn", "harbour": "", "osm_id": 2439330710}, {"lat": 57.6825278, "lon": 11.8710166, "name": "", "harbour": "yes", "osm_id": 2439383664}, {"lat": 57.685711, "lon": 11.8908826, "name": "Nya Varvet", "harbour": "", "osm_id": 2439395466}, {"lat": 57.6876757, "lon": 11.8570344, "name": "", "harbour": "yes", "osm_id": 2439450263}, {"lat": 57.6900073, "lon": 11.8365363, "name": "", "harbour": "yes", "osm_id": 2439453280}, {"lat": 59.2122213, "lon": 10.7857097, "name": "Hank\u00f8 Yacht Club", "harbour": "", "osm_id": 2456763144}, {"lat": 57.7114014, "lon": 11.6826194, "name": "G\u00e4sthamn Kalvsund", "harbour": "", "osm_id": 2670408197}, {"lat": 57.8992109, "lon": 11.7132272, "name": "Tjuvkils hamn", "harbour": "", "osm_id": 2760681485}, {"lat": 59.6442468, "lon": 6.338564, "name": "Sauda Motorb\u00e5t Forening", "harbour": "", "osm_id": 2791789168}, {"lat": 59.0018978, "lon": 5.6597064, "name": "Ryggstranden b\u00e5tforening", "harbour": "", "osm_id": 2795678232}, {"lat": 58.6113335, "lon": 11.2905495, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 2872920807}, {"lat": 58.4323363, "lon": 11.2772352, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 2970496585}, {"lat": 57.7308477, "lon": 11.6580116, "name": "H\u00e4ls\u00f6 hamn", "harbour": "yes", "osm_id": 2981796804}, {"lat": 59.401231, "lon": 8.4891485, "name": "Kviteseid brygge", "harbour": "", "osm_id": 2992147657}, {"lat": 58.315406, "lon": 11.3992014, "name": "Lysekils Marina", "harbour": "", "osm_id": 3003456018}, {"lat": 58.3035672, "lon": 11.3758977, "name": "Stora Korn\u00f6 sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3003532989}, {"lat": 58.2675486, "lon": 11.6828613, "name": "Orust Marina", "harbour": "", "osm_id": 3027099113}, {"lat": 58.2341195, "lon": 6.5310136, "name": "Kongshavn", "harbour": "yes", "osm_id": 3063088735}, {"lat": 58.2551492, "lon": 11.5551195, "name": "Dragsmarks klosters hamn", "harbour": "", "osm_id": 3115492242}, {"lat": 59.6024266, "lon": 5.2096412, "name": "Langev\u00e5g gjestehavn", "harbour": "", "osm_id": 3123604529}, {"lat": 60.90364, "lon": 7.1760244, "name": "", "harbour": "", "osm_id": 3138601977}, {"lat": 60.1801607, "lon": 5.4681906, "name": "", "harbour": "", "osm_id": 3358657843}, {"lat": 57.0571845, "lon": 12.2701893, "name": "Tr\u00e4sl\u00f6vsl\u00e4ges sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3437233292}, {"lat": 57.111074, "lon": 12.2421305, "name": "Varbergs Innerhamn", "harbour": "yes", "osm_id": 3437249767}, {"lat": 57.1131948, "lon": 12.2303903, "name": "Getter\u00f6ns Sm\u00e5b\u00e5tshamn", "harbour": "yes", "osm_id": 3437249768}, {"lat": 60.7883481, "lon": 4.9418469, "name": "Mastrevik kai", "harbour": "", "osm_id": 3440649492}, {"lat": 58.2539739, "lon": 11.5016684, "name": "K\u00e4llvikens och Munkeby marina", "harbour": "", "osm_id": 3484111657}, {"lat": 57.6952544, "lon": 11.687001, "name": "Gr\u00f6t\u00f6 hamn", "harbour": "", "osm_id": 3492063798}, {"lat": 58.286677, "lon": 11.4315098, "name": "L\u00e5ngeviks sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3502067409}, {"lat": 58.2893123, "lon": 11.4344249, "name": "Valbodalen sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3502067432}, {"lat": 58.3052388, "lon": 11.4190188, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3502067458}, {"lat": 58.0344691, "lon": 11.5058716, "name": "Engewikens B\u00e5tf\u00f6rening", "harbour": "yes", "osm_id": 3506243798}, {"lat": 58.0150362, "lon": 11.7981089, "name": "Getsk\u00e4rs Marina", "harbour": "yes", "osm_id": 3507441319}, {"lat": 57.6253328, "lon": 11.9120158, "name": "GFA:s B\u00e5thamn", "harbour": "", "osm_id": 3526074542}, {"lat": 58.8948905, "lon": 11.0138288, "name": "Bopallen", "harbour": "yes", "osm_id": 3527227352}, {"lat": 57.8376709, "lon": 11.6957006, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3531554883}, {"lat": 57.5729223, "lon": 11.927516, "name": "Killingsholmens b\u00e5thamn", "harbour": "", "osm_id": 3532658369}, {"lat": 57.6656193, "lon": 11.8430176, "name": "\u00c4ngholmens Varv", "harbour": "", "osm_id": 3532677619}, {"lat": 57.8094403, "lon": 11.864052, "name": "Varvet i Sj\u00f6hed", "harbour": "", "osm_id": 3532693545}, {"lat": 57.7119259, "lon": 11.7127428, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3532711446}, {"lat": 57.7136961, "lon": 11.7126921, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3532711447}, {"lat": 57.7093601, "lon": 11.6844815, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3532718232}, {"lat": 57.9459459, "lon": 11.5364657, "name": "Kl\u00e4desholmens V\u00e4stra hamn", "harbour": "", "osm_id": 3547388897}, {"lat": 57.9516129, "lon": 11.5599416, "name": "Mossholmens Marina", "harbour": "", "osm_id": 3547388898}, {"lat": 57.9405579, "lon": 11.5785743, "name": "R\u00f6nn\u00e4ngs sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3547434524}, {"lat": 58.2424103, "lon": 11.4611332, "name": "Fiskeb\u00e4ckskils sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3547435944}, {"lat": 58.0365692, "lon": 11.762867, "name": "H\u00f6viksn\u00e4s B\u00e5tf\u00f6rening", "harbour": "", "osm_id": 3547456647}, {"lat": 58.2088679, "lon": 11.4219189, "name": "Grundsunds Marina", "harbour": "", "osm_id": 3547484766}, {"lat": 57.6741843, "lon": 11.6651536, "name": "", "harbour": "yes", "osm_id": 3563186079}, {"lat": 57.7492643, "lon": 11.6552424, "name": "Knippla g\u00e4sthamn", "harbour": "", "osm_id": 3563583594}, {"lat": 57.7575714, "lon": 11.6164939, "name": "Hyppeln g\u00e4sthamn", "harbour": "", "osm_id": 3563604312}, {"lat": 57.7106531, "lon": 11.6878416, "name": "SS Frams g\u00e4sthamn", "harbour": "", "osm_id": 3563626745}, {"lat": 57.3383147, "lon": 12.0891042, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3564509452}, {"lat": 57.3643333, "lon": 12.082014, "name": "G\u00e5rda Brygga", "harbour": "", "osm_id": 3564509880}, {"lat": 57.3869446, "lon": 12.0195176, "name": "Gottsk\u00e4rs Hamn", "harbour": "", "osm_id": 3564510168}, {"lat": 57.3932076, "lon": 12.0237859, "name": "Lindgrens Hamn", "harbour": "", "osm_id": 3564510781}, {"lat": 57.2817399, "lon": 12.1380787, "name": "", "harbour": "yes", "osm_id": 3564591746}, {"lat": 57.4045724, "lon": 12.0305958, "name": "Stora Enen", "harbour": "", "osm_id": 3564612223}, {"lat": 57.6952228, "lon": 11.6656376, "name": "Hedens hamn", "harbour": "", "osm_id": 3564672102}, {"lat": 57.6151896, "lon": 11.7972142, "name": "Styrs\u00f6 Bratten", "harbour": "", "osm_id": 3564709397}, {"lat": 57.6472763, "lon": 11.7852246, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3564709398}, {"lat": 57.3412126, "lon": 12.0965938, "name": "Br\u00e5tavikens B\u00e5ts\u00e4llskap", "harbour": "", "osm_id": 3567460414}, {"lat": 57.5273073, "lon": 11.9255775, "name": "Maleviks hamn", "harbour": "", "osm_id": 3567478463}, {"lat": 57.6939949, "lon": 11.815412, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3583542034}, {"lat": 57.9435742, "lon": 11.542775, "name": "", "harbour": "yes", "osm_id": 3586120098}, {"lat": 57.1159122, "lon": 12.2370007, "name": "Farehamnen", "harbour": "yes", "osm_id": 3586746266}, {"lat": 58.2740708, "lon": 11.4512858, "name": "Sm\u00e5b\u00e5tshamn Kolholmarna/Gr\u00f6t\u00f6", "harbour": "", "osm_id": 3587420744}, {"lat": 58.2698839, "lon": 11.4373581, "name": "Havsbadets g\u00e4sthamn", "harbour": "", "osm_id": 3587426816}, {"lat": 57.6449588, "lon": 11.8580806, "name": "", "harbour": "yes", "osm_id": 3587671914}, {"lat": 57.6999459, "lon": 11.6404592, "name": "R\u00f6ds hamn", "harbour": "", "osm_id": 3587740124}, {"lat": 57.6985676, "lon": 11.6406927, "name": "", "harbour": "yes", "osm_id": 3587740125}, {"lat": 58.0210218, "lon": 11.5177027, "name": "Kyrkesunds \u00d6stra Hamn", "harbour": "", "osm_id": 3591050994}, {"lat": 59.7558079, "lon": 5.7480464, "name": "Arnavik", "harbour": "", "osm_id": 3623011551}, {"lat": 59.2175693, "lon": 10.9179557, "name": "Fjeldberg b\u00e5thavn", "harbour": "", "osm_id": 3638020925}, {"lat": 58.1845838, "lon": 11.4662827, "name": "Ell\u00f6s g\u00e4sthamn", "harbour": "", "osm_id": 3640752946}, {"lat": 58.1503728, "lon": 11.4134425, "name": "Stockens g\u00e4sthamn", "harbour": "", "osm_id": 3640912546}, {"lat": 57.5863138, "lon": 11.921706, "name": "Skintebo hamn", "harbour": "", "osm_id": 3660998630}, {"lat": 58.1224046, "lon": 11.4423404, "name": "H\u00e4lleviksstrands Hamn", "harbour": "", "osm_id": 3667339255}, {"lat": 57.6210756, "lon": 11.7738958, "name": "Styrs\u00f6 T\u00e5ngen", "harbour": "", "osm_id": 3680942427}, {"lat": 58.2761721, "lon": 11.4228969, "name": "Norra Hamnens g\u00e4sthamn", "harbour": "", "osm_id": 3681316450}, {"lat": 57.2993941, "lon": 12.1630827, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3682869148}, {"lat": 57.3161007, "lon": 12.1456648, "name": "Sm\u00e5b\u00e5tshamn", "harbour": "", "osm_id": 3682879687}, {"lat": 57.1372291, "lon": 12.2182793, "name": "S\u00f6dra Hamnen", "harbour": "", "osm_id": 3683010224}], "fairways": [{"type": "way", "coords": [[57.1876667, 11.6481667], [57.0897594, 11.7113334], [56.9915928, 11.7745], [56.8931667, 11.8376667], [56.8826667, 11.7826667], [56.9810927, 11.7193889], [57.0792593, 11.6561111], [57.1771667, 11.5928333], [57.1876667, 11.6481667]], "tags": {"name": "DW Kattegat North", "seamark:fairway:information": "DW Kattegat North", "seamark:fairway:minimum_depth": "19", "seamark:type": "fairway"}, "osm_id": 775428557}, {"type": "way", "coords": [[57.644505, 11.70516], [57.644367, 11.687672], [57.6428395, 11.6878438], [57.640141, 11.687522], [57.6376602, 11.6864276], [57.635018, 11.684797], [57.586685, 11.634135], [57.567346, 11.644006], [57.571857, 11.656344], [57.6063482, 11.6700554], [57.6140494, 11.6794109], [57.638418, 11.704473], [57.644505, 11.70516]], "tags": {"seamark:fairway:information": "DW", "seamark:fairway:minimum_depth": "22.9", "seamark:type": "fairway"}, "osm_id": 1077029174}], "tss": [{"type": "way", "coords": [[61.9, 4.1041667], [61.7297444, 4.1041667], [61.5585428, 4.1041667], [61.3863915, 4.1041667], [61.2132868, 4.1041667], [61.039225, 4.1041667], [60.8642024, 4.1041667], [60.6882153, 4.1041667], [60.5112602, 4.1041667], [60.3333333, 4.1041667]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179980704}, {"type": "way", "coords": [[59.0833333, 3.9745], [58.975, 3.9745]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179980706}, {"type": "way", "coords": [[60.3333333, 3.97], [60.25, 3.9705]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179980708}, {"type": "way", "coords": [[60.3333333, 4.2383333], [60.25, 4.2378333]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179980713}, {"type": "way", "coords": [[59.0833333, 4.2338333], [59.0288333, 4.2338333], [58.975, 4.3325]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179980720}, {"type": "way", "coords": [[59.0833333, 4.072], [58.9971667, 4.072], [58.962, 4.1366667], [59.0833333, 4.1366667], [59.0833333, 4.072]], "tags": {"seamark:name": "TSS Off Utsira", "seamark:type": "separation_zone"}, "osm_id": 179980721}, {"type": "way", "coords": [[60.3333333, 4.0705], [60.25, 4.0708333], [60.25, 4.1375], [60.3333333, 4.1378333], [60.3333333, 4.0705]], "tags": {"seamark:name": "TSS Off Sotra", "seamark:type": "separation_zone"}, "osm_id": 179980723}, {"type": "way", "coords": [[60.25, 4.1041667], [60.0858125, 4.1041667], [59.9208031, 4.1041667], [59.7549692, 4.1041667], [59.5883079, 4.1041667], [59.4208167, 4.1041667], [59.2524927, 4.1041667], [59.0833333, 4.1041667]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179980726}, {"type": "way", "coords": [[58.4921667, 9.4846667], [58.555, 9.5888333]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984909}, {"type": "way", "coords": [[58.405, 9.6766667], [58.4678333, 9.7808333]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984917}, {"type": "way", "coords": [[57.7388333, 6.4466667], [57.6913333, 6.5505], [57.6886667, 6.6875]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984919}, {"type": "way", "coords": [[58.0445, 8.7083333], [58.0621667, 8.772], [58.0908333, 8.8188333]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984921}, {"type": "way", "coords": [[57.7958333, 7.7091667], [57.793, 7.828], [57.8233333, 7.9333333]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984923}, {"type": "way", "coords": [[57.9266667, 8.8258333], [57.9561667, 8.9303333], [58.003, 9.0078333]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984928}, {"type": "way", "coords": [[58.4066667, 5.3695], [58.3733333, 5.4291667], [58.3438333, 5.495]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984931}, {"type": "way", "coords": [[58.3158333, 5.1846667], [58.2766667, 5.2545], [58.2421667, 5.3316667]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984933}, {"type": "way", "coords": [[57.6641667, 7.6953333], [57.6596667, 7.8828333], [57.6653333, 8.0041667]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984940}, {"type": "way", "coords": [[57.84, 6.6086667], [57.8225, 6.6466667], [57.8213333, 6.6975]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984941}, {"type": "way", "coords": [[57.745, 7.9205], [57.8255685, 8.2027222], [57.9059573, 8.4849444], [57.9861667, 8.7671667]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984943}, {"type": "way", "coords": [[58.0463333, 8.9133333], [58.1811198, 9.1354444], [58.3153972, 9.3575556], [58.4491667, 9.5796667]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984944}, {"type": "way", "coords": [[57.7555, 6.6928333], [57.7471131, 7.0283889], [57.7387242, 7.3639444], [57.7303333, 7.6995]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984945}, {"type": "way", "coords": [[58.4378333, 9.6046667], [58.5005, 9.7088333], [58.5221667, 9.6611667], [58.4595, 9.557], [58.4378333, 9.6046667]], "tags": {"seamark:name": "TSS Off Ris\u00f8r", "seamark:type": "separation_zone"}, "osm_id": 179984947}, {"type": "way", "coords": [[57.7133333, 7.6978333], [57.7091667, 7.862], [57.7478333, 7.9986667], [57.7425, 7.8461667], [57.7463333, 7.7016667], [57.7133333, 7.6978333]], "tags": {"seamark:name": "TSS Off Ryvingen", "seamark:type": "separation_zone"}, "osm_id": 179984953}, {"type": "way", "coords": [[58.35, 5.2538333], [58.313, 5.32], [58.2803333, 5.393], [58.3055, 5.4336667], [58.337, 5.3633333], [58.3728333, 5.3], [58.35, 5.2538333]], "tags": {"seamark:name": "TSS Off Egersund", "seamark:type": "separation_zone"}, "osm_id": 179984954}, {"type": "way", "coords": [[58.2933333, 5.4141667], [58.1931673, 5.6368333], [58.0927181, 5.8595], [57.9919852, 6.0821667], [57.8909683, 6.3048333], [57.7896667, 6.5275]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 179984955}, {"type": "way", "coords": [[57.777, 6.5071667], [57.7405, 6.5866667], [57.7383333, 6.6913333], [57.7716667, 6.6936667], [57.7733333, 6.6105], [57.802, 6.5478333], [57.777, 6.5071667]], "tags": {"seamark:name": "TSS Off Farsund", "seamark:type": "separation_zone"}, "osm_id": 179984956}, {"type": "way", "coords": [[57.9708333, 8.782], [57.9958333, 8.8708333], [58.0361667, 8.937], [58.0578333, 8.8896667], [58.0225, 8.8313333], [58.0003333, 8.7525], [57.9708333, 8.782]], "tags": {"seamark:name": "TSS Off Lillesand", "seamark:type": "separation_zone"}, "osm_id": 179984957}, {"type": "way", "coords": [[57.7783333, 10.8343333], [57.7538333, 10.8768333], [57.7481667, 10.8655], [57.7733333, 10.8216667], [57.7786667, 10.8223333], [57.7783333, 10.8343333]], "tags": {"seamark:name": "TSS Skagen East", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 775389707}, {"type": "way", "coords": [[57.8065, 10.8915], [57.7821667, 10.9338333], [57.7763333, 10.9225], [57.8008333, 10.8801667], [57.8065, 10.8915]], "tags": {"seamark:name": "TSS Skagen East", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 775389708}, {"type": "way", "coords": [[57.8291667, 10.9371667], [57.8048333, 10.9795]], "tags": {"name": "TSS Skagen East", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_boundary"}, "osm_id": 775389709}, {"type": "way", "coords": [[57.8688333, 10.5666667], [57.8688333, 10.629]], "tags": {"name": "TSS Skagen West", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_boundary"}, "osm_id": 775389710}, {"type": "way", "coords": [[57.7973333, 10.5373333], [57.7973333, 10.5996667], [57.7943333, 10.5983333], [57.7908333, 10.597], [57.7908333, 10.5346667], [57.7973333, 10.5373333]], "tags": {"name": "TSS Skagen West", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 775389711}, {"type": "way", "coords": [[57.8411667, 10.5553333], [57.825, 10.5486667], [57.825, 10.611], [57.8411667, 10.6176667], [57.8411667, 10.5553333]], "tags": {"seamark:name": "TSS Skagen West", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 775389712}, {"type": "way", "coords": [[57.23, 11.827], [57.2155, 11.8516667], [57.212, 11.8536667]], "tags": {"name": "TSS Fladen", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_boundary"}, "osm_id": 775392701}, {"type": "way", "coords": [[57.2568333, 11.8805], [57.2423333, 11.9051667], [57.2245, 11.9228333]], "tags": {"name": "TSS Fladen", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_boundary"}, "osm_id": 775392702}, {"type": "way", "coords": [[57.2446667, 11.8561667], [57.2301667, 11.8808333], [57.2186667, 11.8915], [57.2175, 11.8851667], [57.2276667, 11.876], [57.2421667, 11.8513333], [57.2446667, 11.8561667]], "tags": {"seamark:name": "TSS Fladen", "seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 775392704}, {"type": "way", "coords": [[59.0910495, 5.5151746], [59.096481, 5.509956]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383461}, {"type": "way", "coords": [[59.1011863, 5.5285216], [59.0957556, 5.5337401]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383463}, {"type": "way", "coords": [[59.0934588, 5.5246791], [59.0988899, 5.5194605], [59.0994789, 5.5217846], [59.0940479, 5.5270032], [59.0934588, 5.5246791]], "tags": {"seamark:type": "separation_zone"}, "osm_id": 1119383464}, {"type": "way", "coords": [[59.1229904, 5.4684877], [59.1415076, 5.5008459]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383472}, {"type": "way", "coords": [[59.1749637, 5.3455997], [59.1750033, 5.3481981], [59.1573992, 5.3492205], [59.1573595, 5.3466221], [59.1749637, 5.3455997]], "tags": {"seamark:type": "separation_zone"}, "osm_id": 1119383475}, {"type": "way", "coords": [[59.1749717, 5.3298497], [59.1569676, 5.3276181]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383476}, {"type": "way", "coords": [[59.1577377, 5.3668212], [59.1749607, 5.3636884]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383477}, {"type": "way", "coords": [[59.0864606, 5.345267], [59.1050472, 5.4086423]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383478}, {"type": "way", "coords": [[59.081983, 5.333408], [59.0818835, 5.3335942], [59.0817908, 5.3337932], [59.0817053, 5.3340041], [59.0816273, 5.3342258], [59.0815574, 5.3344574], [59.0814957, 5.3346977], [59.0814426, 5.3349456], [59.0813983, 5.3352], [59.081363, 5.3354597], [59.0813369, 5.3357235], [59.0813201, 5.3359902], [59.0813127, 5.3362584], [59.0813148, 5.336527], [59.0813262, 5.3367947], [59.081347, 5.3370603], [59.0813771, 5.3373224], [59.0814163, 5.33758], [59.0814644, 5.3378318], [59.0815213, 5.3380766], [59.0815866, 5.3383132], [59.08166, 5.3385407], [59.0817412, 5.3387578], [59.0818299, 5.3389637], [59.0819256, 5.3391573], [59.0820279, 5.3393377], [59.0821362, 5.3395042], [59.0822501, 5.3396558], [59.0823691, 5.339792], [59.0824926, 5.3399121], [59.08262, 5.3400155], [59.0827507, 5.3401017], [59.0828841, 5.3401704], [59.0830196, 5.3402211], [59.0831566, 5.3402538], [59.0832945, 5.3402682], [59.0834325, 5.3402642], [59.08357, 5.3402419], [59.0837065, 5.3402014], [59.0838412, 5.3401429], [59.0839735, 5.3400666], [59.0841028, 5.339973], [59.0842286, 5.3398623], [59.0843502, 5.3397352], [59.0844671, 5.3395923], [59.0845786, 5.3394342], [59.0846844, 5.3392616], [59.0847839, 5.3390754], [59.0848766, 5.3388764], [59.0849621, 5.3386655], [59.08504, 5.3384438], [59.08511, 5.3382122], [59.0851716, 5.3379719], [59.0852248, 5.337724], [59.0852691, 5.3374696], [59.0853043, 5.3372099], [59.0853304, 5.3369461], [59.0853472, 5.3366794], [59.0853546, 5.3364112], [59.0853525, 5.3361426], [59.0853411, 5.3358749], [59.0853203, 5.3356093], [59.0852902, 5.3353471], [59.085251, 5.3350896], [59.0852029, 5.3348378], [59.0851461, 5.334593], [59.0850808, 5.3343563], [59.0850073, 5.3341289], [59.0849261, 5.3339117], [59.0848374, 5.3337059], [59.0847418, 5.3335123], [59.0846395, 5.3333318], [59.0845312, 5.3331654], [59.0844173, 5.3330137], [59.0842983, 5.3328776], [59.0841748, 5.3327575], [59.0840475, 5.3326541], [59.0839167, 5.3325679], [59.0837833, 5.3324992], [59.0836478, 5.3324485], [59.0835108, 5.3324158], [59.083373, 5.3324014], [59.083235, 5.3324054], [59.0830974, 5.3324277], [59.082961, 5.3324682], [59.0828263, 5.3325267], [59.0826939, 5.332603], [59.0825646, 5.3326966], [59.0824388, 5.3328073], [59.0823172, 5.3329344], [59.0822003, 5.3330773], [59.0820887, 5.3332354], [59.081983, 5.333408]], "tags": {"seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 1119383483}, {"type": "way", "coords": [[59.0708282, 5.2935165], [59.0807333, 5.326395]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383484}, {"type": "way", "coords": [[59.0686527, 5.296011], [59.0499802, 5.2326894], [59.0537155, 5.2280009], [59.0730035, 5.2910221], [59.0708282, 5.2935165], [59.0686527, 5.296011]], "tags": {"seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 1119383485}, {"type": "way", "coords": [[59.01968, 5.3438187], [59.0663033, 5.3550196]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383489}, {"type": "way", "coords": [[59.0199617, 5.3279078], [59.0202047, 5.3190672], [59.0620178, 5.3278541], [59.0632919, 5.3354502], [59.0199617, 5.3279078]], "tags": {"seamark:separation_zone:category": "imo_adopted", "seamark:type": "separation_zone"}, "osm_id": 1119383490}, {"type": "way", "coords": [[59.0862567, 5.2753258], [59.0680185, 5.2136564]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383493}, {"type": "way", "coords": [[59.0585316, 5.3076196], [59.0397709, 5.2429032]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383494}, {"type": "way", "coords": [[59.0205526, 5.3011179], [59.0585316, 5.3076196]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 1119383495}]}

meteo_features/osm_piraeus.geojson

@@ -0,0 +1 @@
+{"ports": [{"lat": 37.9626907, "lon": 23.4976751, "name": "", "harbour": "", "osm_id": 1108273564}, {"lat": 37.7182147, "lon": 23.9449553, "name": "Yachts Park \u039b\u03b1\u03c5\u03c1\u03af\u03bf\u03c5", "harbour": "", "osm_id": 1279115192}, {"lat": 38.2356244, "lon": 22.8858061, "name": "", "harbour": "", "osm_id": 1294463423}, {"lat": 38.5115222, "lon": 23.6305308, "name": "\u039d\u03ad\u03b1 \u0391\u03c1\u03c4\u03ac\u03ba\u03b7", "harbour": "", "osm_id": 1416683201}, {"lat": 37.3711023, "lon": 22.7834694, "name": "\u0386\u03b3\u03b9\u03bf\u03c2 \u0391\u03bd\u03b4\u03c1\u03ad\u03b1\u03c2", "harbour": "", "osm_id": 1473400693}, {"lat": 37.9287696, "lon": 23.6303618, "name": "\u038c\u03c1\u03bc\u03bf\u03c2 \u0391\u03c6\u03c1\u03bf\u03b4\u03af\u03c4\u03b7\u03c2", "harbour": "", "osm_id": 1662355431}, {"lat": 37.9730423, "lon": 23.3621623, "name": "", "harbour": "", "osm_id": 1752694835}, {"lat": 38.089429, "lon": 23.1861446, "name": "", "harbour": "", "osm_id": 1756659755}, {"lat": 38.0002562, "lon": 23.4211642, "name": "", "harbour": "", "osm_id": 1828363613}, {"lat": 37.7135806, "lon": 23.3475999, "name": "Angistri", "harbour": "", "osm_id": 2063025342}, {"lat": 37.5196608, "lon": 22.8662526, "name": "", "harbour": "", "osm_id": 2403380502}, {"lat": 37.4780296, "lon": 22.9989081, "name": "", "harbour": "yes", "osm_id": 2662060815}, {"lat": 37.7323628, "lon": 24.0531852, "name": "", "harbour": "", "osm_id": 2720448740}, {"lat": 38.0880938, "lon": 23.9834864, "name": "", "harbour": "", "osm_id": 3009042824}, {"lat": 37.6377046, "lon": 23.1577422, "name": "", "harbour": "", "osm_id": 3166807534}, {"lat": 37.4957357, "lon": 23.4605281, "name": "Greek Sails", "harbour": "", "osm_id": 3631633241}, {"lat": 37.6622818, "lon": 23.9923361, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u039b\u03b5\u03b3\u03c1\u03b5\u03bd\u03ce\u03bd", "harbour": "", "osm_id": 3709583283}, {"lat": 37.2454961, "lon": 22.8686548, "name": "\u039b\u03b9\u03bc\u03ad\u03bd\u03b1\u03c2 \u03a4\u03c5\u03c1\u03bf\u03cd", "harbour": "", "osm_id": 4411807952}, {"lat": 37.9434952, "lon": 23.6379647, "name": "", "harbour": "", "osm_id": 4468874983}, {"lat": 38.0109763, "lon": 22.7530864, "name": "", "harbour": "", "osm_id": 4643084430}, {"lat": 37.4146671, "lon": 22.7665412, "name": "\u039b\u03b9\u03bc\u03ac\u03bd\u03b9 \u0386\u03c3\u03c4\u03c1\u03bf\u03c5\u03c2", "harbour": "yes", "osm_id": 5075251365}, {"lat": 38.4733625, "lon": 23.6025043, "name": "\u038c\u03bc\u03b9\u03bb\u03bf\u03c2 \u03b5\u03c1\u03b1\u03c3\u03b9\u03c4\u03b5\u03c7\u03bd\u03ce\u03bd \u03b1\u03bb\u03b9\u03ad\u03c9\u03bd \"\u0386\u03b3\u03b9\u03bf\u03c2 \u03a4\u03b1\u03be\u03b9\u03ac\u03c1\u03c7\u03b7\u03c2\"", "harbour": "", "osm_id": 5274224463}, {"lat": 38.4728772, "lon": 23.6115678, "name": "", "harbour": "", "osm_id": 5274229868}, {"lat": 38.4609691, "lon": 23.5895345, "name": "Chalkis Commercial Port", "harbour": "", "osm_id": 5291742177}, {"lat": 37.6959739, "lon": 24.0620756, "name": "Olympic Marina", "harbour": "", "osm_id": 5480799656}, {"lat": 37.9787435, "lon": 22.9753417, "name": "", "harbour": "", "osm_id": 6021796484}, {"lat": 37.4975929, "lon": 23.4579295, "name": "\u03a0\u03cc\u03c1\u03bf\u03c2", "harbour": "", "osm_id": 6618569258}, {"lat": 37.4979845, "lon": 23.4566152, "name": "\u03a0\u03cc\u03c1\u03bf\u03c2", "harbour": "", "osm_id": 6618569259}, {"lat": 37.498176, "lon": 23.4550434, "name": "\u03a0\u03cc\u03c1\u03bf\u03c2", "harbour": "", "osm_id": 6618569260}, {"lat": 38.47231, "lon": 23.5996321, "name": "\u038c\u03bc\u03b9\u03bb\u03bf\u03c2 \u03c8\u03b1\u03c1\u03ac\u03b4\u03c9\u03bd \u039a\u03b1\u03bd\u03b1\u03c0\u03af\u03c4\u03c3\u03b1\u03c2 \"\u039f \u03a0\u03bf\u03c3\u03b5\u03b9\u03b4\u03ce\u03bd\"", "harbour": "", "osm_id": 7319759029}, {"lat": 38.0598019, "lon": 23.0996597, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u039c\u03b1\u03c5\u03c1\u03bf\u03bb\u03af\u03bc\u03bd\u03b7\u03c2 \u039a\u03c9\u03bd/\u03bd\u03bf\u03c2 \u03a3\u03ba\u03bf\u03cd\u03c6\u03bf\u03c2", "harbour": "", "osm_id": 7640224695}, {"lat": 37.5832046, "lon": 22.7471441, "name": "\u039b\u03b9\u03bc\u03ac\u03bd\u03b9 \u039d\u03ad\u03b1\u03c2 \u039a\u03af\u03bf\u03c5", "harbour": "", "osm_id": 8325934512}, {"lat": 37.6839595, "lon": 23.3231476, "name": "", "harbour": "", "osm_id": 9055939724}, {"lat": 37.8786703, "lon": 24.0154568, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0391\u03c5\u03bb\u03b1\u03ba\u03af\u03bf\u03c5", "harbour": "", "osm_id": 9724937640}, {"lat": 38.1168239, "lon": 23.9907387, "name": "", "harbour": "", "osm_id": 10971492206}, {"lat": 37.9160397, "lon": 23.0092924, "name": "", "harbour": "yes", "osm_id": 12271331725}, {"lat": 37.9574564, "lon": 23.6127604, "name": "\u0399\u03c7\u03b8\u03c5\u03cc\u03c3\u03ba\u03b1\u03bb\u03b1 \u039a\u03b5\u03c1\u03b1\u03c4\u03c3\u03b9\u03bd\u03af\u03bf\u03c5", "harbour": "", "osm_id": 156707705}, {"lat": 38.1807979, "lon": 24.059924, "name": "\u0391\u03b3\u03af\u03b1 \u039c\u03b1\u03c1\u03af\u03bd\u03b1", "harbour": "", "osm_id": 278197743}, {"lat": 38.230097, "lon": 24.0995231, "name": "", "harbour": "", "osm_id": 278203909}, {"lat": 37.973535, "lon": 24.0113651, "name": "\u0394\u03b7\u03bc\u03bf\u03c4\u03b9\u03ba\u03cc \u039d\u03b1\u03c5\u03c4\u03b1\u03b8\u03bb\u03b7\u03c4\u03b9\u03ba\u03cc \u039a\u03ad\u03bd\u03c4\u03c1\u03bf", "harbour": "", "osm_id": 337354645}, {"lat": 37.879118, "lon": 23.4610726, "name": "", "harbour": "", "osm_id": 353391940}, {"lat": 37.9439793, "lon": 22.933897, "name": "", "harbour": "yes", "osm_id": 366860502}, {"lat": 37.8162587, "lon": 23.8026964, "name": "\u0399\u03c7\u03b8\u03c5\u03cc\u03c3\u03ba\u03b1\u03bb\u03b1 \u0392\u03ac\u03c1\u03ba\u03b9\u03b6\u03b1\u03c2", "harbour": "", "osm_id": 385571909}, {"lat": 37.8708361, "lon": 23.7342089, "name": "4\u03b7 \u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0393\u03bb\u03c5\u03c6\u03ac\u03b4\u03b1\u03c2", "harbour": "", "osm_id": 387683635}, {"lat": 37.6956059, "lon": 24.060045, "name": "", "harbour": "", "osm_id": 401875577}, {"lat": 37.5015188, "lon": 23.4529711, "name": "Stadthafen Poros", "harbour": "", "osm_id": 403503778}, {"lat": 37.690877, "lon": 23.4528665, "name": "\u03a0\u03ad\u03c1\u03b4\u03b9\u03ba\u03b1", "harbour": "", "osm_id": 404738101}, {"lat": 37.8387658, "lon": 23.7614538, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u039d\u03b1\u03c5\u03c4\u03b9\u03ba\u03bf\u03cd \u0391\u03b8\u03bb\u03b7\u03c4\u03b9\u03ba\u03bf\u03cd \u039f\u03bc\u03af\u03bb\u03bf\u03c5 \u0392\u03bf\u03cd\u03bb\u03b1\u03c2", "harbour": "", "osm_id": 409047914}, {"lat": 37.8864447, "lon": 24.0138017, "name": "", "harbour": "", "osm_id": 410587733}, {"lat": 37.8097274, "lon": 23.7753051, "name": "", "harbour": "", "osm_id": 412149510}, {"lat": 37.8102797, "lon": 23.781717, "name": "", "harbour": "", "osm_id": 412149512}, {"lat": 37.5350967, "lon": 22.9170486, "name": "", "harbour": "", "osm_id": 425105927}, {"lat": 37.4780662, "lon": 22.9989441, "name": "Harbor of Iria", "harbour": "", "osm_id": 425106298}, {"lat": 37.8770659, "lon": 23.4414271, "name": "", "harbour": "", "osm_id": 438285717}, {"lat": 38.3904036, "lon": 23.7936445, "name": "", "harbour": "", "osm_id": 445619465}, {"lat": 38.3224384, "lon": 23.795032, "name": "", "harbour": "", "osm_id": 446235566}, {"lat": 38.2954736, "lon": 23.8986645, "name": "", "harbour": "", "osm_id": 446290851}, {"lat": 38.046464, "lon": 23.9995303, "name": "", "harbour": "", "osm_id": 446291241}, {"lat": 38.3857914, "lon": 23.8003358, "name": "", "harbour": "", "osm_id": 447675545}, {"lat": 37.814769, "lon": 23.8419196, "name": "", "harbour": "", "osm_id": 452292422}, {"lat": 37.9319823, "lon": 23.682552, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u03a6\u03bb\u03bf\u03af\u03c3\u03b2\u03bf\u03c5", "harbour": "", "osm_id": 452862736}, {"lat": 37.9012746, "lon": 23.7179107, "name": "", "harbour": "", "osm_id": 452862738}, {"lat": 37.8948771, "lon": 23.7164658, "name": "", "harbour": "", "osm_id": 452862739}, {"lat": 37.8597064, "lon": 23.7488585, "name": "1\u03b7 \u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0393\u03bb\u03c5\u03c6\u03ac\u03b4\u03b1\u03c2", "harbour": "", "osm_id": 452862741}, {"lat": 37.8613306, "lon": 23.7461082, "name": "2\u03b7 \u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0393\u03bb\u03c5\u03c6\u03ac\u03b4\u03b1\u03c2", "harbour": "", "osm_id": 452862745}, {"lat": 37.7099548, "lon": 24.0617481, "name": "\u039b\u03b9\u03bc\u03ac\u03bd\u03b9 \u039b\u03b1\u03c5\u03c1\u03af\u03bf\u03c5", "harbour": "yes", "osm_id": 452865531}, {"lat": 37.9373748, "lon": 23.6603425, "name": "\u039c\u03b9\u03ba\u03c1\u03bf\u03bb\u03af\u03bc\u03b1\u03bd\u03bf", "harbour": "", "osm_id": 452865532}, {"lat": 37.9399454, "lon": 23.6691255, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0391\u03b8\u03b7\u03bd\u03ce\u03bd", "harbour": "", "osm_id": 452865534}, {"lat": 37.9378351, "lon": 23.6865592, "name": "\u039d\u03b1\u03c5\u03c4\u03b1\u03b8\u03bb\u03b7\u03c4\u03b9\u03ba\u03ae \u03bc\u03b1\u03c1\u03af\u03bd\u03b1 \u0394\u03ae\u03bc\u03bf\u03c5 \u039a\u03b1\u03bb\u03bb\u03b9\u03b8\u03ad\u03b1\u03c2", "harbour": "", "osm_id": 452865548}, {"lat": 37.9606201, "lon": 23.5790884, "name": "", "harbour": "", "osm_id": 452935106}, {"lat": 37.8772933, "lon": 23.7269943, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0391\u03b3\u03af\u03bf\u03c5 \u039a\u03bf\u03c3\u03bc\u03ac", "harbour": "", "osm_id": 452935107}, {"lat": 37.9412637, "lon": 22.9358862, "name": "", "harbour": "", "osm_id": 465049152}, {"lat": 37.3477877, "lon": 23.4571082, "name": "", "harbour": "yes", "osm_id": 473489681}, {"lat": 37.3506364, "lon": 23.4658012, "name": "\u038e\u03b4\u03c1\u03b1", "harbour": "yes", "osm_id": 473490345}, {"lat": 37.3536345, "lon": 23.4848971, "name": "", "harbour": "yes", "osm_id": 473942236}, {"lat": 37.5688818, "lon": 22.7965261, "name": "\u039b\u03b9\u03bc\u03ac\u03bd\u03b9 \u039d\u03b1\u03c5\u03c0\u03bb\u03af\u03bf\u03c5", "harbour": "", "osm_id": 495500892}, {"lat": 37.92886, "lon": 24.008359, "name": "", "harbour": "", "osm_id": 500120715}, {"lat": 37.9397958, "lon": 23.538064, "name": "", "harbour": "", "osm_id": 502888344}, {"lat": 37.778146, "lon": 24.0841797, "name": "", "harbour": "", "osm_id": 566799731}, {"lat": 37.7113396, "lon": 24.0585638, "name": "Marina Lavrio", "harbour": "", "osm_id": 587661754}, {"lat": 37.9411011, "lon": 23.662796, "name": "", "harbour": "", "osm_id": 605059761}, {"lat": 37.7725284, "lon": 23.4723022, "name": "", "harbour": "", "osm_id": 613091281}, {"lat": 37.7453757, "lon": 23.4262398, "name": "Aegina Port", "harbour": "", "osm_id": 614791984}, {"lat": 37.7437641, "lon": 23.4279925, "name": "\u039b\u03b9\u03bc\u03ad\u03bd\u03b1\u03c2 \u03a3\u03ba\u03b1\u03c6\u03ce\u03bd \u0391\u03bd\u03b1\u03c8\u03c5\u03c7\u03ae\u03c2", "harbour": "", "osm_id": 614792575}, {"lat": 37.7450726, "lon": 23.4281176, "name": "\u03a0\u03b1\u03bb\u03b1\u03b9\u03cc\u03c2 \u039b\u03b9\u03bc\u03ad\u03bd\u03b1\u03c2", "harbour": "", "osm_id": 616343930}, {"lat": 37.7456622, "lon": 23.4267959, "name": "\u039b\u03b9\u03bc\u03ad\u03bd\u03b1\u03c2 \u03a8\u03b1\u03c1\u03ac\u03b4\u03c9\u03bd", "harbour": "", "osm_id": 616343931}, {"lat": 38.2812531, "lon": 24.1400408, "name": "", "harbour": "", "osm_id": 617632753}, {"lat": 38.2663659, "lon": 24.1580435, "name": "", "harbour": "", "osm_id": 617632755}, {"lat": 37.7543579, "lon": 23.9018235, "name": "", "harbour": "", "osm_id": 654070179}, {"lat": 38.3949254, "lon": 24.0443199, "name": "\u039b\u03b9\u03bc\u03ac\u03bd\u03b9 \u039a\u03b1\u03c1\u03ac\u03b2\u03bf\u03c5", "harbour": "", "osm_id": 701861459}, {"lat": 37.4150769, "lon": 23.3400737, "name": "", "harbour": "", "osm_id": 749792498}, {"lat": 37.4230729, "lon": 23.4844353, "name": "", "harbour": "", "osm_id": 753867077}, {"lat": 38.0368382, "lon": 23.5508953, "name": "", "harbour": "", "osm_id": 763486763}, {"lat": 38.0337698, "lon": 23.5468249, "name": "", "harbour": "", "osm_id": 763486764}, {"lat": 37.9218969, "lon": 23.6948287, "name": "\u03a3\u03cd\u03bb\u03bb\u03bf\u03b3\u03bf\u03c2 \u0391\u03bb\u03b5\u03b9\u03ce\u03bd \u03a0\u03b1\u03bb\u03b1\u03b9\u03bf\u03cd \u03a6\u03b1\u03bb\u03ae\u03c1\u03bf\u03c5", "harbour": "", "osm_id": 785537736}, {"lat": 37.7320788, "lon": 23.4336501, "name": "", "harbour": "", "osm_id": 836141767}, {"lat": 37.7705733, "lon": 23.4549036, "name": "\u039a\u03b1\u03b2\u03bf\u03c5\u03c1\u03cc\u03c0\u03b5\u03c4\u03c1\u03b1", "harbour": "", "osm_id": 836146122}, {"lat": 37.3269276, "lon": 23.1454785, "name": "", "harbour": "", "osm_id": 839678933}, {"lat": 37.3258987, "lon": 23.1467851, "name": "", "harbour": "", "osm_id": 839678934}, {"lat": 37.9140544, "lon": 23.7036731, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u0391\u03bb\u03af\u03bc\u03bf\u03c5", "harbour": "", "osm_id": 885601435}, {"lat": 37.6799338, "lon": 23.1524782, "name": "\u039c\u03b1\u03c1\u03af\u03bd\u03b1 \u039d\u03ad\u03b1\u03c2 \u0395\u03c0\u03b9\u03b4\u03b1\u03cd\u03c1\u03bf\u03c5", "harbour": "", "osm_id": 987013101}, {"lat": 37.5138016, "lon": 22.8576152, "name": "\u039b\u03b9\u03bc\u03ac\u03bd\u03b9 \u03a4\u03bf\u03bb\u03bf\u03cd", "harbour": "", "osm_id": 1005016239}, {"lat": 38.4459373, "lon": 23.6047326, "name": "", "harbour": "", "osm_id": 1029249102}, {"lat": 37.4908901, "lon": 23.8987171, "name": "", "harbour": "", "osm_id": 1039385860}, {"lat": 37.52729, "lon": 22.7314009, "name": "", "harbour": "", "osm_id": 1116406541}], "fairways": [], "tss": [{"type": "way", "coords": [[37.6775281, 23.766009], [37.8450106, 23.6652447]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 195964017}, {"type": "way", "coords": [[37.8217511, 23.5971414], [37.6539982, 23.6982518]], "tags": {"seamark:type": "separation_boundary"}, "osm_id": 195964018}, {"type": "way", "coords": [[37.8369702, 23.6465544], [37.8271579, 23.6180212], [37.6607231, 23.7182083], [37.6711158, 23.7463968], [37.8369702, 23.6465544]], "tags": {"seamark:type": "separation_zone"}, "osm_id": 195964019}]}

scripts/extras/build_meteo_features.py

@@ -0,0 +1,688 @@
+#!/usr/bin/env python3
+"""EnvShip-Bench v2 — Phase 1+2 builder for weather/sea-state/port/TSS context.
+
+Produces 15 new scalar columns per sample (see CC_prompt_lab/prompt_add_wather_wave_prot.md
+for the full schema). Reads existing anchor CSVs, queries
+Open-Meteo Archive + Marine APIs (free, ERA5-derived) plus Overpass for
+OSM seamarks, caches everything to parquet/geojson, and writes
+augmented main CSVs.
+
+Idempotent — re-running skips cached cells, geometry pulls, and CSVs
+that already carry the new columns.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import gzip
+import json
+import math
+import os
+import sys
+import time
+from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+from typing import Iterable
+
+import numpy as np
+import pandas as pd
+import requests
+from shapely.geometry import LineString, Point, Polygon, shape
+from shapely.strtree import STRtree
+
+# ---------------------------------------------------------------------------
+ROOT          = Path(__file__).resolve().parent.parent.parent
+CACHE_DIR     = ROOT / "_meteo_cache"
+WEATHER_DIR   = CACHE_DIR / "weather"
+MARINE_DIR    = CACHE_DIR / "marine"
+OSM_DIR       = CACHE_DIR / "osm"
+SUMMARY_PATH  = CACHE_DIR / "meteo_build_summary.json"
+
+ARCHIVE_URL = "https://archive-api.open-meteo.com/v1/archive"
+MARINE_URL  = "https://marine-api.open-meteo.com/v1/marine"
+OVERPASS_URLS = [
+    "https://overpass-api.de/api/interpreter",
+    "https://overpass.openstreetmap.fr/api/interpreter",
+]
+
+WEATHER_VARS = [
+    "wind_speed_10m", "wind_direction_10m",
+    "temperature_2m", "surface_pressure", "cloud_cover",
+]
+MARINE_VARS = [
+    "wave_height", "wave_direction", "wave_period",
+    "swell_wave_height",
+]
+
+GRID_DEG = 0.25  # ERA5-matched
+
+NEW_COLS = [
+    "met_wind_speed_mps", "met_wind_dir_deg", "met_wind_rel_heading_deg",
+    "met_temperature_c", "met_pressure_hpa", "met_cloud_cover_pct",
+    "sea_wave_height_m", "sea_wave_dir_deg", "sea_wave_period_s", "sea_swell_wave_height_m",
+    "port_nearest_dist_km", "port_nearest_name",
+    "in_fairway", "dist_to_fairway_m", "in_tss",
+]
+
+SUBSETS = {
+    "DMA":     {
+        "root":         "/mnt/nfs/kun/DeepJSCC/ship_trajectory_datesets",
+        "hf_dir_a":     "track_a_short-term_Cross-domain_Datasets/dma_track_v1",
+        "hf_dir_b":     "track_b_medium-term_Cross-domain_Datasets/dma/standard_track_v1",
+    },
+    "NOAA":    {
+        "root":         "/mnt/nfs/kun/DeepJSCC/NOAA_ship_trajectory_datasets",
+        "hf_dir_a":     "track_a_short-term_Cross-domain_Datasets/noaa_track_v1",
+        "hf_dir_b":     "track_b_medium-term_Cross-domain_Datasets/noaa/standard_track_v1",
+    },
+    "Piraeus": {
+        "root":         "Piraeus_ship_trajectory_datasets",
+        "hf_dir_a":     "track_a_short-term_Cross-domain_Datasets/piraeus_track_v1",
+        "hf_dir_b":     "track_b_medium-term_Cross-domain_Datasets/piraeus/standard_track_v1",
+    },
+    "Norway":  {
+        "root":         "norway_ship_trajectory_datasets",
+        "hf_dir_a":     "track_a_short-term_Cross-domain_Datasets/norway_track_v1",
+        "hf_dir_b":     "track_b_medium-term_Cross-domain_Datasets/norway/standard_track_v1",
+    },
+}
+
+# ---------------------------------------------------------------------------
+# Utilities
+# ---------------------------------------------------------------------------
+
+def log(msg: str) -> None:
+    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
+
+
+def cell_of(lat: float, lon: float, grid: float = GRID_DEG) -> tuple[float, float]:
+    return (round(lat / grid) * grid, round(lon / grid) * grid)
+
+
+def cell_id(lat: float, lon: float) -> str:
+    return f"{lat:+.3f}_{lon:+.3f}"
+
+
+def month_key(ts: str) -> str:
+    """ts in form 2025-09-04T09:47:20+00:00 → '2025-09'."""
+    return ts[:7]
+
+
+def month_bounds(ym: str) -> tuple[str, str]:
+    from datetime import date, timedelta
+    y, m = ym.split("-")
+    first = date(int(y), int(m), 1)
+    nxt = date(int(y)+1, 1, 1) if m == "12" else date(int(y), int(m)+1, 1)
+    last = nxt - timedelta(days=1)
+    return first.isoformat(), last.isoformat()
+
+
+def haversine_km(lat1, lon1, lat2, lon2) -> float:
+    R = 6371.0
+    rl1, rl2 = math.radians(lat1), math.radians(lat2)
+    dlat = math.radians(lat2 - lat1); dlon = math.radians(lon2 - lon1)
+    a = math.sin(dlat/2)**2 + math.cos(rl1)*math.cos(rl2)*math.sin(dlon/2)**2
+    return 2 * R * math.asin(math.sqrt(a))
+
+
+def signed_angle_deg(a: float, b: float) -> float:
+    """Shortest signed angle a-b in [-180, 180]."""
+    d = (a - b + 540.0) % 360.0 - 180.0
+    return d
+
+
+# ---------------------------------------------------------------------------
+# Cell enumeration
+# ---------------------------------------------------------------------------
+
+def _load_anchors_from(anchors_csv: Path) -> list[dict]:
+    out = []
+    if not anchors_csv.exists():
+        return out
+    with anchors_csv.open() as fh:
+        for row in csv.DictReader(fh):
+            try:
+                lat = float(row["anchor_lat"]); lon = float(row["anchor_lon"])
+                if lat == 0 and lon == 0:
+                    continue
+                out.append({
+                    "sample_id":   row["sample_id"],
+                    "lat":         lat,
+                    "lon":         lon,
+                    "ts":          row["hist_end_ts"],
+                })
+            except Exception:
+                pass
+    return out
+
+
+def load_anchors(root: str) -> list[dict]:
+    """Load Track-A anchors for a subset (sample_id → lat/lon/ts)."""
+    return _load_anchors_from(Path(root) / "multi_type_mini_bench_build/standard_track_v1/context_v1/environment/anchors/all_anchors.csv")
+
+
+def load_anchors_track_b(subset_name: str) -> list[dict]:
+    """Load Track-B anchors for a subset."""
+    return _load_anchors_from(ROOT / "track_b" / subset_name / "multi_type_mini_bench_build/standard_track_v1/context_v1/environment/anchors/all_anchors.csv")
+
+
+# ---------------------------------------------------------------------------
+# Open-Meteo fetch
+# ---------------------------------------------------------------------------
+
+def _save_pq(df: pd.DataFrame, path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    df.to_parquet(path, index=False)
+
+
+def fetch_open_meteo(url: str, lat: float, lon: float, start: str, end: str,
+                     hourly: list[str], retries: int = 3, sleep_s: float = 1.0) -> pd.DataFrame | None:
+    params = {
+        "latitude": lat, "longitude": lon,
+        "start_date": start, "end_date": end,
+        "hourly": ",".join(hourly),
+        "timezone": "UTC",
+    }
+    last_err = None
+    for attempt in range(retries):
+        try:
+            r = requests.get(url, params=params, timeout=60)
+            if r.status_code == 429:
+                time.sleep(60); continue
+            r.raise_for_status()
+            j = r.json()
+            h = j.get("hourly", {})
+            if not h or "time" not in h:
+                return None
+            df = pd.DataFrame(h)
+            df["time"] = pd.to_datetime(df["time"], utc=True)
+            time.sleep(sleep_s)
+            return df
+        except Exception as e:
+            last_err = e
+            time.sleep(min(30.0, 2 ** attempt))
+    log(f"[open-meteo] FAIL ({lat:.3f},{lon:.3f},{start}): {last_err}")
+    return None
+
+
+def fetch_cell_months(cell_months: set[tuple[float,float,str]],
+                      base_dir: Path, url: str, hourly: list[str],
+                      max_workers: int = 4) -> None:
+    base_dir.mkdir(parents=True, exist_ok=True)
+    pending = []
+    for lat, lon, ym in cell_months:
+        cache = base_dir / ym / f"{cell_id(lat, lon)}.parquet"
+        if cache.exists() and cache.stat().st_size > 0:
+            continue
+        pending.append((lat, lon, ym, cache))
+    if not pending:
+        return
+    log(f"[fetch] {base_dir.name}: {len(pending):,} cell-months pending")
+    t0 = time.time()
+    done = 0
+    def _task(lat, lon, ym, cache):
+        start, end = month_bounds(ym)
+        df = fetch_open_meteo(url, lat, lon, start, end, hourly)
+        if df is not None:
+            _save_pq(df, cache)
+        return df is not None
+    with ThreadPoolExecutor(max_workers=max_workers) as ex:
+        futures = [ex.submit(_task, *p) for p in pending]
+        for f in as_completed(futures):
+            done += 1
+            if done % 50 == 0:
+                eta = (time.time()-t0)/done * (len(pending)-done)
+                log(f"  {done}/{len(pending)} done  eta {eta:.0f}s")
+    log(f"[fetch] {base_dir.name} done in {time.time()-t0:.0f}s")
+
+
+# ---------------------------------------------------------------------------
+# OSM ports + fairways via Overpass
+# ---------------------------------------------------------------------------
+
+def overpass_query(query: str, retries: int = 3) -> dict | None:
+    last_err = None
+    for url in OVERPASS_URLS:
+        for attempt in range(retries):
+            try:
+                r = requests.post(url, data=query.encode("utf-8"),
+                                  headers={"Content-Type":"text/plain",
+                                           "User-Agent":"EnvShip-Bench/v2 meteo build"},
+                                  timeout=120)
+                if r.status_code == 429:
+                    time.sleep(60); continue
+                r.raise_for_status()
+                return r.json()
+            except Exception as e:
+                last_err = e
+                time.sleep(min(20.0, 5 * (attempt + 1)))
+    log(f"[overpass] FAILED: {last_err}")
+    return None
+
+
+def fetch_osm_layers(name: str, bbox: tuple[float,float,float,float],
+                     anchor_cells: set | None = None) -> dict:
+    """Return {'ports': [{...}], 'fairways': [{...}], 'tss': [{...}]}.
+
+    For subsets with a wide bbox (NOAA), use the actual anchor 0.25° cells
+    grouped into 5° super-bins → only query bboxes where samples exist.
+    Avoids hundreds of empty-ocean chunks.
+    """
+    s, w, n, e = bbox
+    layers = {"ports": [], "fairways": [], "tss": []}
+    out_dir = OSM_DIR
+    out_dir.mkdir(parents=True, exist_ok=True)
+    cache = out_dir / f"{name.lower()}.geojson"
+    if cache.exists() and cache.stat().st_size > 100:
+        try:
+            return json.loads(cache.read_text())
+        except Exception:
+            pass
+
+    # Default: one bbox covering everything.
+    boxes = [(s, w, n, e)]
+    is_wide = (e - w) > 30 or (n - s) > 30
+    if is_wide and anchor_cells:
+        # Group 0.25° cells into 5° bins; one bbox per non-empty bin (with small pad).
+        bins: dict[tuple[float,float], list[tuple[float,float]]] = defaultdict(list)
+        for la, lo in anchor_cells:
+            bins[(round(la/5)*5, round(lo/5)*5)].append((la, lo))
+        boxes = []
+        for _, cells in bins.items():
+            lats = [c[0] for c in cells]; lons = [c[1] for c in cells]
+            boxes.append((min(lats) - 0.25, min(lons) - 0.25,
+                          max(lats) + 0.5,  max(lons) + 0.5))
+        log(f"[osm:{name}] wide bbox → {len(boxes)} anchor-clustered sub-boxes")
+    elif is_wide:
+        # Fallback: uniform split
+        lat_steps = max(1, int(math.ceil((n - s) / 10)))
+        lon_steps = max(1, int(math.ceil((e - w) / 10)))
+        boxes = []
+        for i in range(lat_steps):
+            for j in range(lon_steps):
+                ss = s + (n - s) * i / lat_steps
+                nn = s + (n - s) * (i + 1) / lat_steps
+                ww = w + (e - w) * j / lon_steps
+                ee = w + (e - w) * (j + 1) / lon_steps
+                boxes.append((ss, ww, nn, ee))
+        log(f"[osm:{name}] wide bbox → {len(boxes)} uniform chunks")
+
+    for idx, (ss, ww, nn, ee) in enumerate(boxes, 1):
+        log(f"[osm:{name}] chunk {idx}/{len(boxes)}  bbox=({ss:.2f},{ww:.2f},{nn:.2f},{ee:.2f})")
+        # 1) ports
+        q_port = f"""[out:json][timeout:120];
+(
+  way["harbour"~"yes|sea|marina|fishing"]({ss},{ww},{nn},{ee});
+  way["seamark:type"="harbour"]({ss},{ww},{nn},{ee});
+  way["landuse"="harbour"]({ss},{ww},{nn},{ee});
+  node["harbour"~"yes|sea|marina"]({ss},{ww},{nn},{ee});
+  node["seamark:type"="harbour"]({ss},{ww},{nn},{ee});
+);
+out tags center 200;"""
+        j = overpass_query(q_port)
+        if j:
+            for el in j.get("elements", []):
+                lat = el.get("lat") or el.get("center", {}).get("lat")
+                lon = el.get("lon") or el.get("center", {}).get("lon")
+                if lat is None or lon is None: continue
+                tags = el.get("tags", {})
+                layers["ports"].append({
+                    "lat": lat, "lon": lon,
+                    "name": tags.get("name", ""),
+                    "harbour": tags.get("harbour", ""),
+                    "osm_id": el.get("id"),
+                })
+        # 2) fairways
+        q_fw = f"""[out:json][timeout:120];
+(
+  way["seamark:type"="fairway"]({ss},{ww},{nn},{ee});
+  relation["seamark:type"="fairway"]({ss},{ww},{nn},{ee});
+);
+out geom 500;"""
+        j = overpass_query(q_fw)
+        if j:
+            for el in j.get("elements", []):
+                if el.get("type") == "way":
+                    geom = el.get("geometry", [])
+                    if len(geom) >= 2:
+                        coords = [(g["lat"], g["lon"]) for g in geom]
+                        layers["fairways"].append({"type":"way","coords":coords,
+                                                   "tags":el.get("tags",{}),
+                                                   "osm_id":el.get("id")})
+        # 3) TSS
+        q_tss = f"""[out:json][timeout:120];
+(
+  way["seamark:type"~"separation_zone|separation_line|separation_boundary"]({ss},{ww},{nn},{ee});
+  relation["seamark:type"~"separation_zone|separation_line|separation_boundary"]({ss},{ww},{nn},{ee});
+);
+out geom 500;"""
+        j = overpass_query(q_tss)
+        if j:
+            for el in j.get("elements", []):
+                if el.get("type") == "way":
+                    geom = el.get("geometry", [])
+                    if len(geom) >= 2:
+                        coords = [(g["lat"], g["lon"]) for g in geom]
+                        layers["tss"].append({"type":"way","coords":coords,
+                                              "tags":el.get("tags",{}),
+                                              "osm_id":el.get("id")})
+
+    cache.write_text(json.dumps(layers))
+    log(f"[osm:{name}]  ports={len(layers['ports'])}  fairways={len(layers['fairways'])}  tss={len(layers['tss'])}")
+    return layers
+
+
+# ---------------------------------------------------------------------------
+# Build local-meter STRtree from layers for distance queries
+# ---------------------------------------------------------------------------
+
+def build_geom_index(layers: dict) -> dict:
+    """Return {'port_tree': STRtree, 'port_items': [...], 'fairway_tree': STRtree,
+              'fairway_items': [...], 'tss_tree': STRtree, 'tss_items': [...]}.
+    Geometries are in (lon, lat) deg space (shapely treats them as planar).
+    Distance computed via haversine in the query.
+    """
+    out = {}
+    # ports — points
+    port_pts = []
+    for p in layers.get("ports", []):
+        port_pts.append((Point(p["lon"], p["lat"]), p))
+    if port_pts:
+        out["port_tree"]  = STRtree([g for g,_ in port_pts])
+        out["port_items"] = [m for _,m in port_pts]
+    else:
+        out["port_tree"], out["port_items"] = None, []
+    # fairways — lines
+    fw_items = []
+    for f in layers.get("fairways", []):
+        coords = [(lo, la) for la, lo in f["coords"]]
+        try:
+            fw_items.append((LineString(coords), f))
+        except Exception:
+            continue
+    if fw_items:
+        out["fairway_tree"]  = STRtree([g for g,_ in fw_items])
+        out["fairway_items"] = [m for _,m in fw_items]
+    else:
+        out["fairway_tree"], out["fairway_items"] = None, []
+    # TSS — polygons or lines
+    tss_items = []
+    for t in layers.get("tss", []):
+        coords = [(lo, la) for la, lo in t["coords"]]
+        try:
+            if coords[0] == coords[-1] and len(coords) >= 4:
+                tss_items.append((Polygon(coords), t))
+            else:
+                tss_items.append((LineString(coords), t))
+        except Exception:
+            continue
+    if tss_items:
+        out["tss_tree"]  = STRtree([g for g,_ in tss_items])
+        out["tss_items"] = [m for _,m in tss_items]
+    else:
+        out["tss_tree"], out["tss_items"] = None, []
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Cell-month → cell × hour lookup
+# ---------------------------------------------------------------------------
+
+def load_grid_for_subset(base_dir: Path, cell_months: set) -> dict:
+    """Return {(round(lat,3), round(lon,3)) → DataFrame indexed by hour}."""
+    out = {}
+    for lat, lon, ym in cell_months:
+        cache = base_dir / ym / f"{cell_id(lat,lon)}.parquet"
+        if not cache.exists():
+            continue
+        try:
+            df = pd.read_parquet(cache)
+        except Exception:
+            continue
+        df = df.set_index("time")
+        key = (round(lat, 3), round(lon, 3))
+        if key in out:
+            out[key] = pd.concat([out[key], df]).sort_index()
+            out[key] = out[key][~out[key].index.duplicated(keep='last')]
+        else:
+            out[key] = df
+    return out
+
+
+# ---------------------------------------------------------------------------
+# Per-sample column compute
+# ---------------------------------------------------------------------------
+
+def per_sample_columns(sid: str, lat: float, lon: float, ts_str: str,
+                       wx: dict, mr: dict,
+                       geom: dict, cog_deg: float | None) -> dict:
+    """Return a dict of the 15 new columns for one sample."""
+    out = {c: None for c in NEW_COLS}
+    # Cell + hour
+    cell = cell_of(lat, lon)
+    key = (round(cell[0],3), round(cell[1],3))
+    ts = pd.Timestamp(ts_str).tz_convert("UTC").floor("h") if pd.Timestamp(ts_str).tzinfo else \
+         pd.Timestamp(ts_str, tz="UTC").floor("h")
+    # Weather
+    df_w = wx.get(key)
+    if df_w is not None and ts in df_w.index:
+        row = df_w.loc[ts]
+        ws_kmh = row.get("wind_speed_10m")
+        if ws_kmh is not None and not pd.isna(ws_kmh):
+            out["met_wind_speed_mps"] = float(ws_kmh) / 3.6
+        wd = row.get("wind_direction_10m")
+        if wd is not None and not pd.isna(wd):
+            out["met_wind_dir_deg"] = float(wd)
+            if cog_deg is not None:
+                out["met_wind_rel_heading_deg"] = signed_angle_deg(float(wd), cog_deg)
+        for src, dst in [("temperature_2m","met_temperature_c"),
+                         ("surface_pressure","met_pressure_hpa"),
+                         ("cloud_cover","met_cloud_cover_pct")]:
+            v = row.get(src)
+            if v is not None and not pd.isna(v):
+                out[dst] = float(v)
+    # Marine
+    df_m = mr.get(key)
+    if df_m is not None and ts in df_m.index:
+        row = df_m.loc[ts]
+        for src, dst in [("wave_height","sea_wave_height_m"),
+                         ("wave_direction","sea_wave_dir_deg"),
+                         ("wave_period","sea_wave_period_s"),
+                         ("swell_wave_height","sea_swell_wave_height_m")]:
+            v = row.get(src)
+            if v is not None and not pd.isna(v):
+                out[dst] = float(v)
+    # Ports
+    if geom["port_tree"] is not None:
+        pt = Point(lon, lat)
+        idxs = geom["port_tree"].query_nearest(pt)
+        if len(idxs):
+            best = None; best_d = float("inf")
+            for ii in idxs[:5]:
+                p = geom["port_items"][int(ii)]
+                d = haversine_km(lat, lon, p["lat"], p["lon"])
+                if d < best_d: best_d, best = d, p
+            if best is not None:
+                out["port_nearest_dist_km"] = float(best_d)
+                out["port_nearest_name"]    = best.get("name","")
+    # Fairway
+    if geom["fairway_tree"] is not None:
+        pt = Point(lon, lat)
+        idxs = geom["fairway_tree"].query_nearest(pt)
+        if len(idxs):
+            # Distance from point to nearest fairway line (in deg space then converted)
+            best_geom = geom["fairway_items"][int(idxs[0])]
+            line = LineString([(lo, la) for la, lo in best_geom["coords"]])
+            np_pt = line.interpolate(line.project(pt))
+            d_km  = haversine_km(lat, lon, np_pt.y, np_pt.x)
+            d_m   = d_km * 1000.0
+            out["dist_to_fairway_m"] = float(d_m)
+            out["in_fairway"]        = bool(d_m <= 100.0)
+    else:
+        out["in_fairway"] = False
+    # TSS
+    in_tss = False
+    if geom["tss_tree"] is not None:
+        pt = Point(lon, lat)
+        idxs = geom["tss_tree"].query(pt, predicate="intersects")
+        if len(idxs):
+            in_tss = True
+    out["in_tss"] = bool(in_tss)
+    return out
+
+
+# ---------------------------------------------------------------------------
+# CSV augmentation
+# ---------------------------------------------------------------------------
+
+def cog_from_row(row: dict) -> float | None:
+    """Return the last history COG in degrees, computed from
+    hist_cog_sin_json / hist_cog_cos_json arrays."""
+    try:
+        s = json.loads(row["hist_cog_sin_json"])
+        c = json.loads(row["hist_cog_cos_json"])
+        sin = s[-1]; cos = c[-1]
+        deg = (math.degrees(math.atan2(sin, cos)) + 360.0) % 360.0
+        return deg
+    except Exception:
+        return None
+
+
+def augment_csv(csv_in: Path, csv_out: Path, wx: dict, mr: dict, geom: dict,
+                anchor_map: dict) -> dict:
+    """Return stats {rows, nulls_per_col, written}."""
+    nulls = {c: 0 for c in NEW_COLS}
+    n_total = 0
+    with gzip.open(csv_in, "rt", encoding="utf-8", newline="") as ih:
+        reader = csv.DictReader(ih)
+        base_fields = list(reader.fieldnames or [])
+        # Drop already-present new cols (idempotent re-run)
+        out_fields = [f for f in base_fields if f not in NEW_COLS] + NEW_COLS
+        with gzip.open(csv_out, "wt", encoding="utf-8", newline="") as oh:
+            writer = csv.DictWriter(oh, fieldnames=out_fields)
+            writer.writeheader()
+            for row in reader:
+                n_total += 1
+                sid = row["sample_id"]
+                anchor = anchor_map.get(sid)
+                if anchor is None:
+                    new = {c: "" for c in NEW_COLS}
+                else:
+                    cog = cog_from_row(row)
+                    cols = per_sample_columns(sid, anchor["lat"], anchor["lon"],
+                                              anchor["ts"], wx, mr, geom, cog)
+                    new = {}
+                    for c in NEW_COLS:
+                        v = cols.get(c)
+                        if v is None or (isinstance(v, float) and math.isnan(v)):
+                            new[c] = ""
+                            nulls[c] += 1
+                        else:
+                            new[c] = v
+                base = {k: row.get(k, "") for k in out_fields if k not in NEW_COLS}
+                base.update(new)
+                writer.writerow(base)
+    return {"rows": n_total, "nulls": nulls, "written": str(csv_out)}
+
+
+# ---------------------------------------------------------------------------
+# Subset driver
+# ---------------------------------------------------------------------------
+
+def process_subset(name: str) -> dict:
+    cfg = SUBSETS[name]
+    root_a = Path(cfg["root"])
+    log(f"=== {name} ===")
+    anchors = load_anchors(cfg["root"])
+    if not anchors:
+        log(f"  no anchors → skip"); return {"name": name, "skipped": True}
+    log(f"  anchors loaded: {len(anchors):,}")
+    cell_months = set()
+    bbox = [180,90,-180,-90]  # w,s,e,n
+    for a in anchors:
+        c = cell_of(a["lat"], a["lon"])
+        cell_months.add((c[0], c[1], month_key(a["ts"])))
+        bbox[0] = min(bbox[0], a["lon"]); bbox[1] = min(bbox[1], a["lat"])
+        bbox[2] = max(bbox[2], a["lon"]); bbox[3] = max(bbox[3], a["lat"])
+    log(f"  unique cell-months: {len(cell_months):,}")
+    # Fetch weather + marine
+    fetch_cell_months(cell_months, WEATHER_DIR / name, ARCHIVE_URL, WEATHER_VARS, max_workers=4)
+    fetch_cell_months(cell_months, MARINE_DIR  / name, MARINE_URL,  MARINE_VARS,  max_workers=4)
+    # Load grids
+    wx = load_grid_for_subset(WEATHER_DIR / name, cell_months)
+    mr = load_grid_for_subset(MARINE_DIR  / name, cell_months)
+    log(f"  loaded wx cells: {len(wx)}  marine cells: {len(mr)}")
+    # OSM layers + STRtree
+    pad = 0.5
+    anchor_cells = {(c[0], c[1]) for (c0, c1, _) in cell_months for c in [(c0, c1)]}
+    layers = fetch_osm_layers(
+        name,
+        (bbox[1]-pad, bbox[0]-pad, bbox[3]+pad, bbox[2]+pad),
+        anchor_cells=anchor_cells,
+    )
+    geom = build_geom_index(layers)
+    anchor_map = {a["sample_id"]: a for a in anchors}
+    # Augment Track A
+    summary = {"name": name, "track_a": {}, "track_b": {}}
+    for split in ("train","val","test"):
+        in_path  = root_a / f"multi_type_mini_bench_build/standard_track_v1/{split}/part-000.csv.gz"
+        if not in_path.exists():
+            log(f"  TrackA/{split} missing — skip"); continue
+        out_tmp = in_path.with_suffix(".csv.gz.tmp")
+        stats = augment_csv(in_path, out_tmp, wx, mr, geom, anchor_map)
+        out_tmp.replace(in_path)
+        summary["track_a"][split] = stats
+        log(f"  TrackA/{split}: {stats['rows']:,} rows, "
+            f"null_wind={stats['nulls']['met_wind_speed_mps']}, "
+            f"null_wave={stats['nulls']['sea_wave_height_m']}, "
+            f"null_port_km={stats['nulls']['port_nearest_dist_km']}")
+    # Track B (has its own anchors)
+    tb_root = ROOT / "track_b" / name / "multi_type_mini_bench_build/standard_track_v1"
+    if tb_root.exists():
+        anchors_b = load_anchors_track_b(name)
+        anchor_map_b = {a["sample_id"]: a for a in anchors_b}
+        # Add any Track-B cells/months that we did not fetch before
+        extra = set()
+        for a in anchors_b:
+            c = cell_of(a["lat"], a["lon"]); extra.add((c[0], c[1], month_key(a["ts"])))
+        new_cm = extra - cell_months
+        if new_cm:
+            log(f"  TrackB extra cell-months: {len(new_cm):,}")
+            fetch_cell_months(new_cm, WEATHER_DIR/name, ARCHIVE_URL, WEATHER_VARS, max_workers=4)
+            fetch_cell_months(new_cm, MARINE_DIR/name,  MARINE_URL,  MARINE_VARS,  max_workers=4)
+            wx.update(load_grid_for_subset(WEATHER_DIR/name, new_cm))
+            mr.update(load_grid_for_subset(MARINE_DIR/name,  new_cm))
+        log(f"  TrackB anchors loaded: {len(anchors_b):,}")
+        for split in ("train","val","test"):
+            in_path = tb_root / split / "part-000.csv.gz"
+            if not in_path.exists():
+                continue
+            out_tmp = in_path.with_suffix(".csv.gz.tmp")
+            stats = augment_csv(in_path, out_tmp, wx, mr, geom, anchor_map_b)
+            out_tmp.replace(in_path)
+            summary["track_b"][split] = stats
+            log(f"  TrackB/{split}: {stats['rows']:,} rows, "
+                f"null_wind={stats['nulls']['met_wind_speed_mps']}, "
+                f"null_port={stats['nulls']['port_nearest_dist_km']}")
+    return summary
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--subsets", nargs="+", default=list(SUBSETS.keys()))
+    p.add_argument("--skip-fetch", action="store_true",
+                   help="Re-use existing parquet/geojson cache, only re-run merge.")
+    args = p.parse_args()
+
+    summaries = {}
+    for s in args.subsets:
+        if s not in SUBSETS:
+            log(f"unknown subset {s}"); continue
+        summaries[s] = process_subset(s)
+    SUMMARY_PATH.write_text(json.dumps(summaries, indent=2, default=str))
+    log(f"build summary → {SUMMARY_PATH}")
+
+
+if __name__ == "__main__":
+    main()

scripts/extras/pbf_to_tile_cache.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+"""Convert a Geofabrik OSM PBF extract into the per-tile Overpass-JSON
+cache format expected by build_standard_track_context_v1 (used inside the EnvShip-Bench build pipeline).
+
+For each tile (0.25 degree by default), emit a JSON file at:
+  <out_cache>/tiles/<TILE_ID>.json
+
+where TILE_ID matches the convention used by the build script:
+    f"{tile_lat:+08.3f}_{tile_lon:+09.3f}"
+
+The JSON structure mirrors the Overpass API output that the build script's
+_parse_ways() consumes — list of {"type": "way", "id": ..., "nodes": [...],
+"tags": {"natural":"coastline"} | {"natural":"water"} |
+{"man_made":"pier"|"breakwater"|"groyne"|"quay"}, "geometry": [{"lat":..,
+"lon":..}, ...]} entries inside payload["elements"].
+
+This script extracts only the way geometry types that the build script
+queries via Overpass — keeping the cache size small and the parse fast.
+
+Usage
+-----
+python pbf_to_tile_cache.py \
+    --pbf <path.osm.pbf> \
+    --out-cache <env/osm_cache or alt path> \
+    --bbox south,west,north,east \
+    [--tiles-list <file with one TILE_ID per line>] \
+    [--tile-deg 0.25]
+
+Either --bbox (full coverage) or --tiles-list (selective) MUST be given.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import sys
+import time
+from collections import defaultdict
+from pathlib import Path
+from typing import Iterable
+
+import osmium
+
+
+def tile_id_for_point(lat: float, lon: float, tile_deg: float) -> str:
+    tile_lat = math.floor(lat / tile_deg) * tile_deg
+    tile_lon = math.floor(lon / tile_deg) * tile_deg
+    return f"{tile_lat:+08.3f}_{tile_lon:+09.3f}"
+
+
+# Tags accepted, matching _make_overpass_query() in build_standard_track_context_v1 (used inside the EnvShip-Bench build pipeline)
+def is_target_way(tags: dict) -> bool:
+    if not tags:
+        return False
+    nat = tags.get("natural", "")
+    mm  = tags.get("man_made", "")
+    if nat == "coastline":
+        return True
+    if nat == "water":
+        return True
+    if mm in ("pier", "breakwater", "groyne", "quay"):
+        return True
+    return False
+
+
+class WayCollector(osmium.SimpleHandler):
+    """Collect target ways with geometry into per-tile buckets.
+
+    osmium gives us node coordinates by attaching a location cache before
+    parsing. The SimpleHandler base class supports `apply_file(filename,
+    locations=True)` which fills the cache automatically.
+    """
+
+    def __init__(self, tile_deg: float, accept_tiles: set | None = None,
+                 bbox: tuple | None = None):
+        super().__init__()
+        self.tile_deg = tile_deg
+        self.accept_tiles = accept_tiles
+        self.bbox = bbox  # (south, west, north, east) or None
+        self.tile_payload: dict[str, list[dict]] = defaultdict(list)
+        self.way_count = 0
+        self.kept_count = 0
+        self.t0 = time.time()
+
+    def way(self, w):
+        self.way_count += 1
+        if self.way_count % 100000 == 0:
+            sys.stderr.write(
+                f"[pbf] ways scanned={self.way_count:,}  kept={self.kept_count:,}  "
+                f"elapsed={time.time()-self.t0:.0f}s\n")
+        tags = {tag.k: tag.v for tag in w.tags}
+        if not is_target_way(tags):
+            return
+        try:
+            coords = []
+            for n in w.nodes:
+                # osmium WayNode -> location
+                loc = n.location
+                if loc.valid():
+                    coords.append((loc.lat, loc.lon))
+        except osmium.InvalidLocationError:
+            return  # unresolved node coords; skip
+        if len(coords) < 2:
+            return
+        # bbox filter
+        if self.bbox is not None:
+            s, ww, n, ee = self.bbox
+            lats = [c[0] for c in coords]; lons = [c[1] for c in coords]
+            if max(lats) < s or min(lats) > n or max(lons) < ww or min(lons) > ee:
+                return
+        # Group by tile: assign way to every tile that any node falls in
+        node_tiles = set()
+        for lat, lon in coords:
+            node_tiles.add(tile_id_for_point(lat, lon, self.tile_deg))
+        if self.accept_tiles is not None:
+            node_tiles &= self.accept_tiles
+            if not node_tiles:
+                return
+        elem = {
+            "type": "way",
+            "id":   w.id,
+            "tags": tags,
+            "geometry": [{"lat": float(lat), "lon": float(lon)} for lat, lon in coords],
+        }
+        for tid in node_tiles:
+            self.tile_payload[tid].append(elem)
+        self.kept_count += 1
+
+
+def write_tile_jsons(payload_map: dict[str, list[dict]], out_root: Path) -> int:
+    tiles_dir = out_root / "tiles"
+    tiles_dir.mkdir(parents=True, exist_ok=True)
+    n = 0
+    for tid, elements in payload_map.items():
+        path = tiles_dir / f"{tid}.json"
+        payload = {
+            "version": 0.6,
+            "generator": "pbf_to_tile_cache.py",
+            "osm3s": {
+                "copyright":
+                    "The data included in this document is from www.openstreetmap.org. "
+                    "Available under the Open Database License (ODbL).",
+            },
+            "elements": elements,
+        }
+        path.write_text(json.dumps(payload))
+        n += 1
+    return n
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--pbf",     type=Path, required=True)
+    p.add_argument("--out-cache", type=Path, required=True,
+                   help="Destination root; tiles/ subdir will be created.")
+    p.add_argument("--bbox",    type=str, default=None,
+                   help="south,west,north,east — restrict to this area.")
+    p.add_argument("--tiles-list", type=Path, default=None,
+                   help="One TILE_ID per line; only emit these tiles. "
+                        "If absent emit all tiles intersected by ways within --bbox.")
+    p.add_argument("--tile-deg", type=float, default=0.25)
+    args = p.parse_args()
+
+    accept_tiles = None
+    if args.tiles_list:
+        accept_tiles = {ln.strip() for ln in args.tiles_list.read_text().splitlines() if ln.strip()}
+        print(f"[pbf] accept_tiles loaded: {len(accept_tiles):,}", flush=True)
+    bbox = None
+    if args.bbox:
+        s, ww, n, ee = [float(x) for x in args.bbox.split(",")]
+        bbox = (s, ww, n, ee)
+        print(f"[pbf] bbox=({s},{ww},{n},{ee})", flush=True)
+
+    coll = WayCollector(args.tile_deg, accept_tiles=accept_tiles, bbox=bbox)
+    print(f"[pbf] parsing {args.pbf}  size={args.pbf.stat().st_size/(1<<20):.0f} MiB", flush=True)
+    # locations=True attaches the node location cache (in-RAM dense index by id)
+    # idx="sparse_mem_array" is more compact for partial PBFs; use sparse_mmap_array
+    # for big planet files.
+    coll.apply_file(str(args.pbf), locations=True, idx="sparse_mem_array")
+    print(f"[pbf] DONE  ways={coll.way_count:,}  kept={coll.kept_count:,}  "
+          f"tiles_with_data={len(coll.tile_payload):,}", flush=True)
+
+    written = write_tile_jsons(coll.tile_payload, args.out_cache)
+    print(f"[pbf] wrote {written:,} tile JSON files under {args.out_cache}/tiles/", flush=True)
+
+
+if __name__ == "__main__":
+    main()

scripts/extras/stage_17_osm_temporal_consistency.py

@@ -0,0 +1,241 @@
+#!/usr/bin/env python3
+"""Stage 17 — OSM temporal-consistency check for standard_track samples.
+
+The OSM rasters used to build the env context come from a single OSM
+snapshot (typically the current 2026 OSM). When the AIS data is older
+than the OSM snapshot (e.g. Piraeus 2019 vs OSM 2026), recently-built
+ports/piers/breakwaters appear as land in the SDF while the original
+AIS trajectory was on water. This stage flags such samples.
+
+For each sample:
+  1. Decode hist+fut XY arrays back to meters relative to anchor.
+  2. Project each (x, y) onto the per-sample signed_dist_shore raster.
+  3. signed_dist_shore < 0 means the point is on land per the OSM snapshot.
+  4. Aggregate:
+        max_inland_depth_m       — deepest inland penetration in meters
+        n_inland_points          — number of trajectory points flagged inland
+        max_consec_inland_run    — longest run of consecutive inland points
+        n_uncheckable_points     — points outside the 5-km SDF patch (Track B)
+  5. Flag:
+        osm_temporal_consistent  = (max_inland_depth_m <= MAX_DEPTH_M)
+                                   AND (max_consec_inland_run < MAX_RUN)
+
+Defaults: MAX_DEPTH_M = 30, MAX_RUN = 3. Both are configurable.
+
+Output: <track>/osm_temporal_consistency/<split>_flags.csv per dataset.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import gzip
+import json
+import math
+from pathlib import Path
+from typing import Iterable
+
+import numpy as np
+
+GRID = 128
+RADIUS_M = 5000.0
+CELL_M = (2 * RADIUS_M) / GRID  # ≈ 78.125 m
+
+
+def xy_to_cell(x: float, y: float, r: float = RADIUS_M, g: int = GRID):
+    """Match the orientation used by build_standard_track_context_v1.py:
+       gx = floor((x + r) / (2r) * g)
+       gy = floor((r - y) / (2r) * g)   # flipped vertically
+    """
+    gx = int(math.floor((x + r) / (2 * r) * g))
+    gy = int(math.floor((r - y) / (2 * r) * g))
+    return gx, gy
+
+
+def longest_consecutive_run(flags: Iterable[bool]) -> int:
+    """Length of the longest run of consecutive True values."""
+    best = cur = 0
+    for f in flags:
+        if f:
+            cur += 1
+            best = max(best, cur)
+        else:
+            cur = 0
+    return best
+
+
+def process_split(
+    track_root: Path,
+    context_root: Path,
+    split: str,
+    out_dir: Path,
+    max_depth_m: float,
+    max_run_threshold: int,
+) -> dict:
+    """Process a single split. Returns aggregate counters."""
+    sdf_shore_path = context_root / "environment" / "rasters" / split / "signed_dist_shore.npy"
+    sample_ids_path = context_root / "environment" / "rasters" / split / "sample_ids.npy"
+    csv_path = track_root / split / "part-000.csv.gz"
+
+    if not (sdf_shore_path.exists() and sample_ids_path.exists() and csv_path.exists()):
+        return {"split": split, "skipped": True, "reason": "missing inputs"}
+
+    sdf = np.load(sdf_shore_path)             # (N, g, g) float32 meters
+    sample_ids = np.load(sample_ids_path, allow_pickle=True)
+    id_to_row = {str(sid): idx for idx, sid in enumerate(sample_ids)}
+
+    out_dir.mkdir(parents=True, exist_ok=True)
+    flag_path = out_dir / f"{split}_flags.csv"
+    fields = [
+        "sample_id",
+        "n_hist_points", "n_fut_points",
+        "n_hist_inland", "n_fut_inland", "n_inland_points",
+        "n_uncheckable_points",
+        "max_inland_depth_m", "max_consec_inland_run",
+        "any_anchor_inland", "anchor_inland_depth_m",
+        "osm_temporal_consistent",
+    ]
+    total = 0
+    n_consistent = 0
+    n_uncheckable_only = 0
+    n_no_sdf = 0
+
+    with gzip.open(csv_path, "rt", encoding="utf-8", newline="") as fh, \
+         flag_path.open("w", newline="", encoding="utf-8") as out_fh:
+        reader = csv.DictReader(fh)
+        writer = csv.DictWriter(out_fh, fieldnames=fields)
+        writer.writeheader()
+        for row in reader:
+            sid = row["sample_id"]
+            row_idx = id_to_row.get(sid)
+            if row_idx is None:
+                n_no_sdf += 1
+                writer.writerow({
+                    "sample_id": sid,
+                    "n_hist_points": 0, "n_fut_points": 0,
+                    "n_hist_inland": 0, "n_fut_inland": 0, "n_inland_points": 0,
+                    "n_uncheckable_points": 0,
+                    "max_inland_depth_m": 0.0, "max_consec_inland_run": 0,
+                    "any_anchor_inland": "",
+                    "anchor_inland_depth_m": "",
+                    "osm_temporal_consistent": "",   # uncheckable
+                })
+                total += 1
+                continue
+            try:
+                hist_x = json.loads(row["hist_x_json"])
+                hist_y = json.loads(row["hist_y_json"])
+                fut_x  = json.loads(row["fut_x_json"])
+                fut_y  = json.loads(row["fut_y_json"])
+            except Exception:
+                n_no_sdf += 1
+                continue
+            sdf_patch = sdf[row_idx]  # (g, g)
+            ordered_flags = []
+            hist_inland = 0
+            fut_inland = 0
+            max_depth = 0.0
+            n_uncheckable = 0
+            for i, (x, y) in enumerate(zip(hist_x, hist_y)):
+                gx, gy = xy_to_cell(x, y)
+                if 0 <= gx < GRID and 0 <= gy < GRID:
+                    shore = float(sdf_patch[gy, gx])
+                    inland = shore < 0
+                    if inland:
+                        depth = -shore
+                        max_depth = max(max_depth, depth)
+                        hist_inland += 1
+                    ordered_flags.append(inland)
+                else:
+                    n_uncheckable += 1
+                    ordered_flags.append(False)
+            for i, (x, y) in enumerate(zip(fut_x, fut_y)):
+                gx, gy = xy_to_cell(x, y)
+                if 0 <= gx < GRID and 0 <= gy < GRID:
+                    shore = float(sdf_patch[gy, gx])
+                    inland = shore < 0
+                    if inland:
+                        depth = -shore
+                        max_depth = max(max_depth, depth)
+                        fut_inland += 1
+                    ordered_flags.append(inland)
+                else:
+                    n_uncheckable += 1
+                    ordered_flags.append(False)
+            n_total_pts = len(ordered_flags)
+            n_inland = hist_inland + fut_inland
+            n_check = n_total_pts - n_uncheckable
+            max_run_len = longest_consecutive_run(ordered_flags)
+            # Anchor (centre of patch) — cell (g/2, g/2)
+            anchor_shore = float(sdf_patch[GRID // 2, GRID // 2])
+            anchor_inland = anchor_shore < 0
+            consistent = (max_depth <= max_depth_m) and (max_run_len < max_run_threshold)
+            # Edge case: if every point was outside patch (uncheckable), flag as None
+            if n_check == 0:
+                consistent_str = ""           # uncheckable
+                n_uncheckable_only += 1
+            else:
+                consistent_str = "true" if consistent else "false"
+                if consistent:
+                    n_consistent += 1
+            writer.writerow({
+                "sample_id": sid,
+                "n_hist_points": len(hist_x), "n_fut_points": len(fut_x),
+                "n_hist_inland": hist_inland, "n_fut_inland": fut_inland,
+                "n_inland_points": n_inland,
+                "n_uncheckable_points": n_uncheckable,
+                "max_inland_depth_m": f"{max_depth:.2f}",
+                "max_consec_inland_run": max_run_len,
+                "any_anchor_inland": "true" if anchor_inland else "false",
+                "anchor_inland_depth_m": f"{(-anchor_shore):.2f}" if anchor_inland else "0.00",
+                "osm_temporal_consistent": consistent_str,
+            })
+            total += 1
+
+    return {
+        "split": split,
+        "total": total,
+        "consistent": n_consistent,
+        "uncheckable_only": n_uncheckable_only,
+        "no_sdf": n_no_sdf,
+        "flag_path": str(flag_path),
+    }
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--track-root",   type=Path, required=True,
+                   help="standard_track_v1 root (with train/val/test/part-000.csv.gz)")
+    p.add_argument("--context-root", type=Path, required=True,
+                   help="context_v1 root (containing environment/rasters/<split>/)")
+    p.add_argument("--output-dir",   type=Path, required=True,
+                   help="Where to write <split>_flags.csv + summary.json")
+    p.add_argument("--max-depth-m", type=float, default=30.0,
+                   help="Tolerance: max inland depth allowed [m]")
+    p.add_argument("--max-run",     type=int,   default=3,
+                   help="Tolerance: max consecutive inland points")
+    p.add_argument("--splits", nargs="+", default=["train", "val", "test"])
+    args = p.parse_args()
+
+    print(f"[stage17] track={args.track_root}")
+    print(f"[stage17] context={args.context_root}")
+    print(f"[stage17] threshold: max_depth_m={args.max_depth_m}  max_run={args.max_run}")
+    all_stats = {}
+    for split in args.splits:
+        print(f"[stage17] split={split} ...", flush=True)
+        stats = process_split(args.track_root, args.context_root, split,
+                              args.output_dir, args.max_depth_m, args.max_run)
+        all_stats[split] = stats
+        print(f"[stage17]   {stats}", flush=True)
+    summary = {
+        "track_root":   str(args.track_root),
+        "context_root": str(args.context_root),
+        "max_depth_m":  args.max_depth_m,
+        "max_run":      args.max_run,
+        "splits":       all_stats,
+    }
+    (args.output_dir / "summary.json").write_text(json.dumps(summary, indent=2))
+    print(f"[stage17] DONE → {args.output_dir}/summary.json")
+
+
+if __name__ == "__main__":
+    main()

scripts/extras/taxonomy.py

@@ -0,0 +1,127 @@
+"""Unified 7+1-class taxonomy for cross-domain benchmark.
+
+Canonical classes (in benchmark order; integer ID = index):
+    0 unknown
+    1 cargo            (ITU 70-79 + DMA text 'cargo', 'bulk', 'container', 'ro-ro cargo')
+    2 tanker           (ITU 80-89 + DMA 'tanker')
+    3 passenger        (ITU 60-69 + DMA 'passenger', 'ferry', 'cruise')
+    4 fishing          (ITU 30          + DMA 'fishing')
+    5 tug              (ITU 31, 32, 52  + DMA 'tug', 'tow')
+    6 service          (ITU 33, 34, 50, 51, 53-59 + DMA 'pilot', 'service', 'port tender', 'sar', 'law enforcement', 'anti-pollution')
+    7 sailing_leisure  (ITU 35, 36, 37  + DMA 'sailing', 'pleasure', 'yacht')
+
+Differs from the original DMA enum (which had `ferry` as a separate class and merged
+`sailing_leisure` into `unknown`) — unified to give cross-dataset evaluations a
+single class axis to score against.
+"""
+from __future__ import annotations
+
+import re
+
+CLASSES = ("unknown","cargo","tanker","passenger","fishing","tug","service","sailing_leisure")
+CLASS_TO_ID = {c: i for i, c in enumerate(CLASSES)}
+
+
+def _normalize(text: object) -> str:
+    if text is None:
+        return ""
+    s = str(text).strip().lower()
+    return re.sub(r"\s+", " ", s)
+
+
+def from_itu_code(value: object) -> str | None:
+    """Map an ITU AIS ship-type code (0–99) to a unified class."""
+    if value is None:
+        return None
+    try:
+        code = int(float(str(value).strip()))
+    except (ValueError, TypeError):
+        return None
+    if not (0 <= code <= 99):
+        return None
+    if 70 <= code <= 79: return "cargo"
+    if 80 <= code <= 89: return "tanker"
+    if 60 <= code <= 69: return "passenger"
+    if code == 30: return "fishing"
+    if code in (31, 32, 52): return "tug"
+    if code in (33, 34, 50, 51, 53, 54, 55, 56, 57, 58, 59): return "service"
+    if code in (35, 36, 37): return "sailing_leisure"
+    return "unknown"
+
+
+_TEXT_RULES = (
+    # (token-set, class) — first match wins
+    (("ro-ro cargo", "container", "containership", "bulk carrier", "bulk cargo",
+      "general cargo", "refrigerated cargo", "cargo,", "cargo "), "cargo"),
+    (("oil tanker", "products tanker", "chemical tanker", "lng tanker", "lpg tanker",
+      "shuttle tanker", "tanker"), "tanker"),
+    (("passenger/ro-ro", "passenger/cruise", "cruise ship", "ferry", "passenger ship",
+      "passenger,", "passenger "), "passenger"),
+    (("fishing vessel", "fishing", "trawler", "fish factory"), "fishing"),
+    (("tug,", "tug ", "tug/supply", "towing", "pusher"), "tug"),
+    (("pilot", "search and rescue", "sar", "anti-pollution", "law enforcement",
+      "port tender", "diving", "buoy/lighthouse", "research", "supply", "service",
+      "offshore", "well stimulation", "crew boat", "icebreaker"), "service"),
+    (("yacht", "sailing vessel", "sailing", "pleasure craft", "leisure"), "sailing_leisure"),
+)
+
+
+def from_text(value: object) -> str | None:
+    """Map a free-text vessel-type string (DMA / VesselFinder / MarineTraffic style)
+    to a unified class."""
+    text = _normalize(value)
+    if not text:
+        return None
+    for tokens, cls in _TEXT_RULES:
+        for tok in tokens:
+            if tok in text:
+                return cls
+    if text in ("unknown", "undefined", "not defined", "unknown value", "other"):
+        return "unknown"
+    return None
+
+
+def unify(value: object) -> str:
+    """Return one of the 8 canonical class names. Accepts ITU code, text label,
+    or already-unified class. Falls back to 'unknown'."""
+    if value is None:
+        return "unknown"
+    s = str(value).strip().lower()
+    if not s:
+        return "unknown"
+    # Direct hit?
+    if s in CLASS_TO_ID:
+        return s
+    # ITU code?
+    try:
+        code = int(float(s))
+        cls = from_itu_code(code)
+        if cls:
+            return cls
+    except (ValueError, TypeError):
+        pass
+    # Text rule?
+    cls = from_text(s)
+    if cls:
+        return cls
+    return "unknown"
+
+
+def unify_id(value: object) -> int:
+    return CLASS_TO_ID[unify(value)]
+
+
+if __name__ == "__main__":
+    # quick self-test
+    cases = [
+        (70, "cargo"), ("80", "tanker"), ("60.0", "passenger"), (30, "fishing"),
+        (52, "tug"), (50, "service"), (35, "sailing_leisure"), (0, "unknown"),
+        ("Oil Products Tanker", "tanker"), ("Container Ship", "cargo"),
+        ("Cargo, hazard B (X)", "cargo"), ("Passenger", "passenger"),
+        ("Pleasure Craft", "sailing_leisure"), ("Pilot Vessel", "service"),
+        ("Fishing", "fishing"), ("Tug", "tug"), (None, "unknown"), ("", "unknown"),
+    ]
+    for v, expected in cases:
+        got = unify(v)
+        ok = "✓" if got == expected else "✗"
+        print(f"  {ok} unify({v!r}) -> {got}  (expected {expected})")

track_a_short-term_Cross-domain_Datasets/dma_track_v1/README.md

@@ -0,0 +1,109 @@
+# Standard Track v1 — EnvShip-Bench
+
+Quality-stratified multi-type compact benchmark subset, built from `benchmark/core`.
+
+## Scale
+
+| Split | Samples | Unique MMSI | Unique Segments |
+|-------|--------:|------------:|----------------:|
+| Train | 120,000 | 3,307       | 18,710          |
+| Val   |  15,000 | 366         | 1,908           |
+| Test  |  15,000 | 341         | 1,997           |
+
+**Total: 150,000 samples**
+
+## Design
+
+Forecasting protocol (same as full benchmark):
+- Observation: 10 min (30 points × 20 s)
+- Prediction: 10 min (30 points × 20 s)
+- Evaluation unit: meters
+
+Key differences vs `clean_ship_core_lite_v1`:
+- 6.25× larger (150K vs 24K total)
+- 5 vessel types vs 2
+- Explicit difficulty tiers: Easy / Medium / Hard
+- Relaxed quality thresholds to include curves and maneuvers
+- Mean difficulty score 6.12 vs 3.90 (57% harder)
+- Bridge-turn angle 6.78° vs 1.68° (4× more turning)
+- Hard-tier samples: 12.7% vs ~0%
+
+## Vessel Type Composition
+
+The DMA (Danish Maritime Authority) data is cargo-dominated.
+Actual distribution in train:
+
+| Type             | Train   | %     |
+|------------------|--------:|------:|
+| cargo_tanker     | 93,659  | 78.0% |
+| fishing          | 11,466  |  9.6% |
+| sailing_leisure  |  7,892  |  6.6% |
+| passenger_ferry  |  3,879  |  3.2% |
+| tug_service      |  3,104  |  2.6% |
+
+Note: target fractions were aspirational; actual fractions reflect
+the DMA source data (Danish waters, September 2025).
+
+## Difficulty Tier Distribution
+
+| Tier   | difficulty_score range | Train   | %     |
+|--------|------------------------|--------:|------:|
+| easy   | < 5.0                  | 72,144  | 60.1% |
+| medium | 5.0 – 12.0             | 32,609  | 27.2% |
+| hard   | ≥ 12.0                 | 15,247  | 12.7% |
+
+## Quality Profile (Train)
+
+| Metric                     | Mean   | Median | P90    |
+|----------------------------|-------:|-------:|-------:|
+| quality_score              | 93.71  | 95.04  | 97.06  |
+| difficulty_score           |  6.12  |  3.92  | 15.18  |
+| avg_speed_knots            | 10.15  |  9.92  | 14.73  |
+| future_linearity           | 0.9625 | 0.9664 | 0.9681 |
+| fut_efficiency             | 0.9968 | 1.0000 | 1.0000 |
+| bridge_turn_deg            |  6.78  |  2.22  | 18.82  |
+| fut_turn_mean_abs_deg      |  0.938 |  0.520 |  2.447 |
+| interp_ratio_total         | 0.0015 |  0.0   |  0.0   |
+
+## Diversity Constraints (Builder Parameters)
+
+| Parameter                  | Train | Val/Test |
+|----------------------------|------:|---------:|
+| max_per_mmsi               |    55 |       70 |
+| max_per_segment            |    12 |       20 |
+| min_segment_step_gap       |     2 |        3 |
+
+## Artifacts
+
+```
+standard_track_v1/
+├── train/part-000.csv.gz   (120,000 samples, 97 MB compressed)
+├── val/part-000.csv.gz     ( 15,000 samples, 12 MB compressed)
+├── test/part-000.csv.gz    ( 15,000 samples, 12 MB compressed)
+├── sample_ids/
+│   ├── train_sample_ids.txt
+│   ├── val_sample_ids.txt
+│   └── test_sample_ids.txt
+├── reports/
+│   ├── train_report.json
+│   ├── val_report.json
+│   └── test_report.json
+└── summary.json
+```
+
+## Reproduce
+
+```bash
+cd /mnt/nfs/kun/DeepJSCC/ship_trajectory_datesets/multi_type_mini_bench_build
+python3 build_standard_track_v1.py \
+  --benchmark-root /mnt/nfs/kun/DeepJSCC/ship_trajectory_datesets/benchmark/core \
+  --output-root ./standard_track_v1 \
+  --train-target 120000 --val-target 15000 --test-target 15000
+```
+
+## Known Limitations
+
+- Single geographic region: Danish waters only (DMA AIS, September 2025)
+- Single month: no seasonal variation
+- Cargo-tanker dominance (78% train) reflects source data demographics
+- Social and environment context not yet computed for this subset

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/README.md

@@ -0,0 +1,133 @@
+# context_v1 — Environment + Social Context for Standard Track v1
+
+Unified context package built from Standard Track v1 (150K samples).
+Provides environment-aware and social-interaction-aware annotations for every sample.
+
+## Structure
+
+```
+context_v1/
+├── environment/               — Geographic context
+│   ├── anchors/               — Anchor lat/lon recovered from stage10
+│   ├── features/{split}/environment_descriptors.csv
+│   ├── rasters/{split}/
+│   │   ├── masks.npy              (N×6×128×128 uint8)
+│   │   ├── signed_dist_shore.npy  (N×128×128 float16)
+│   │   ├── signed_dist_nav.npy    (N×128×128 float16)
+│   │   └── sample_ids.npy
+│   ├── vectors/{split}/vectors.jsonl.gz
+│   ├── feature_stats.json
+│   └── summary.json
+├── social/                    — Nearby-vessel interaction context
+│   ├── features/{split}/social_features.csv
+│   ├── _snapshot_buckets/     — Compact per-timestamp AIS snapshots
+│   └── summary.json
+├── augmented/{split}/part-000.csv.gz  — Trajectory + env + social merged
+└── summary.json
+```
+
+## Environment Package
+
+**Setup:** 5 km patch radius, 128×128 raster grid, OSM-derived
+
+Raster channels (masks.npy, dim 0):
+1. `land_mask`        — binary, 1 = land
+2. `water_mask`       — binary, 1 = water (flood-fill from anchor)
+3. `geo_navigable_mask` — binary, 1 = navigable (water not on barrier)
+4. `natural_boundary_mask` — coastline, riverbank
+5. `manmade_boundary_mask` — pier, breakwater, quay, port
+6. `barrier_mask`     — max(natural, manmade)
+
+Additional arrays:
+- `signed_dist_shore.npy` — signed distance to shore/barrier (positive = water side)
+- `signed_dist_nav.npy`   — signed distance to navigable region
+
+Descriptor columns (features/*/environment_descriptors.csv):
+- `scene_type`: open_water / nearshore / harbor / constrained
+- `water_ratio`, `navigable_ratio`, `barrier_density`
+- `nearest_shore_dist_m`, `nearest_manmade_dist_m`, `nearest_barrier_dist_m`
+- `anchor_in_water`, `anchor_in_navigable`, `anchor_on_barrier`
+- `env_quality_score`, `scene_open_water`, `scene_nearshore`, `scene_harbor`, `scene_constrained`
+
+### Scene Type Distribution
+
+| Scene | Train | Val | Test |
+|-------|------:|----:|-----:|
+| open_water | 115,278 (96.1%) | 14,447 (96.3%) | 14,509 (96.7%) |
+| nearshore | 3,135 (2.6%) | 350 (2.3%) | 273 (1.8%) |
+| constrained | 1,427 (1.2%) | 184 (1.2%) | 203 (1.4%) |
+| harbor | 160 (0.1%) | 19 (0.1%) | 15 (0.1%) |
+
+Note: 188 OSM tiles failed to fetch (406 from Overpass, not in local cache). These are treated
+as open water and are mostly offshore tiles — the open water classification is correct for them.
+
+### Environment Quality
+
+- Mean env_quality_score: 0.986 (all splits) — very high
+- anchor_in_water: 99.6% — nearly all anchors are correctly in water
+
+## Social Context Package
+
+**Setup:** 3 km radius, max 10 neighbors, interaction threshold ≥2 neighbors
+
+Social features per sample (social/features/*/social_features.csv):
+- `target_global_lat`, `target_global_lon` — recovered absolute position
+- `neighbor_count_total`, `neighbor_count_used`
+- `neighbor_density_bin`: isolated / sparse / medium / dense
+- `neighbor_mmsi_json`, `neighbor_ship_type_json`
+- `neighbor_distance_m_json` — distances to each neighbor (sorted)
+- `neighbor_rel_x_m_json`, `neighbor_rel_y_m_json` — relative positions in local meters
+- `neighbor_rel_vx_mps_json`, `neighbor_rel_vy_mps_json` — relative velocities
+- `neighbor_cpa_m_json`, `neighbor_tcpa_s_json` — CPA and TCPA per neighbor
+- `min_neighbor_distance_m`, `mean_neighbor_distance_m`
+- `min_cpa_m`, `min_abs_tcpa_s`
+- `social_context_available`, `interaction_candidate`
+
+### Social Density Distribution
+
+| Metric | Train | Val | Test |
+|--------|------:|----:|-----:|
+| Samples | 120,000 | 15,000 | 15,000 |
+| With ≥1 neighbor | 24,716 (20.6%) | 2,942 (19.6%) | 3,072 (20.5%) |
+| Interaction candidates (≥2) | 4,889 (4.1%) | 575 (3.8%) | 482 (3.2%) |
+| Mean neighbor count | 0.265 | 0.257 | 0.243 |
+| Dense (≥7 neighbors) | 80 | 28 | 10 |
+
+Social context is intentionally sparse (~80% isolated) because this is a quality-first
+benchmark selecting for clean straight-line cruise trajectories. Dense interaction samples
+can be found in `social_benchmark_mini` which was built specifically for interaction modeling.
+
+## Augmented CSV
+
+`augmented/{split}/part-000.csv.gz` merges all context with the original trajectory:
+- **89 columns total**: 39 trajectory + 17 environment + 21 social + 12 meta
+- Train: 102.8 MB, Val: 12.6 MB, Test: 12.6 MB
+
+## Storage
+
+| Component | Size |
+|-----------|-----:|
+| Rasters (masks + distances) | 1.7 GB |
+| Vectors (OSM jsonl.gz) | 1.3 GB |
+| Social (features + snapshots) | 813 MB |
+| Augmented CSVs | 128 MB |
+| Features + anchors | 8 MB |
+| **Total** | **~3.8 GB** |
+
+## Reproducing
+
+```bash
+cd /mnt/nfs/kun/DeepJSCC/ship_trajectory_datesets/multi_type_mini_bench_build
+python3 build_standard_track_context_v1.py \
+  --track-root   ./standard_track_v1 \
+  --stage10-root /mnt/nfs/kun/DeepJSCC/ship_trajectory_datesets/data_interim/10_minlen_filtered/partitions \
+  --osm-cache-dir /mnt/nfs/kun/DeepJSCC/ship_trajectory_datesets/mini_benchmark/clean_ship_core_lite_v1/environment_v2/osm_cache/tiles \
+  --output-dir   ./standard_track_v1/context_v1
+```
+
+## Known Limitations
+
+- 188 OSM tile fetch failures (406 Overpass error); treated as open water
+- 96% of samples are offshore (open water) — environment context is sparse for most samples
+- Social context is target-centric (only current-state neighbors, no neighbor history)
+- Stage10 data covers September 2025 (DMA) only — no multi-month social patterns

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/augmented/test/part-000.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6055d67b098863a8ee01a7c98c146c34f54025cec8e778f032b1186c8bf91e3b
+size 13190732

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/augmented/train/part-000.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8a1f76e4bbb2be19949f5edc9140d34a48c90f84df6e415ea9dc1bf0658f7e2
+size 107781369

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/augmented/val/part-000.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6970d0b2f6bf4e71795797de284c8f88108e1f2814c624b8bf4b9304ba0e3cd0
+size 13221289

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/all_environment_descriptors.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82988113c1c9f74d0fcde104125cf237ab7e99cdcdbfc36f85d4f6645c0769f5
+size 35043148

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/anchor_lookup.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0cb9023a4ae6f180d81f0b6ca11abd7ed47fe7572595292242071cbbe2d96af7
+size 25507488

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/anchors/all_anchors.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3239a12b7bce9e2b27324e26d5ce43497d049bc1acfc990bcfd53adfb2f25932
+size 25974325

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/anchors/train_anchors.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9266a1c2ac9316d47a0de3d16dc16ecf8fbd86a226f5300851069c3c6f53c5b3
+size 20818548

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/failed_tiles.json

@@ -0,0 +1,10 @@
+[
+  {
+    "tile_id": "+055.750_+0003.000",
+    "error": "OSM fetch failed for +055.750_+0003.000: 429 Client Error: Too Many Requests for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0018.000",
+    "error": "OSM fetch failed for +057.500_+0018.000: 504 Server Error: Gateway Timeout for url: https://overpass-api.de/api/interpreter"
+  }
+]

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/failed_tiles.pre_refill.json

@@ -0,0 +1,754 @@
+[
+  {
+    "tile_id": "+052.750_+0003.000",
+    "error": "OSM fetch failed for +052.750_+0003.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+052.750_+0003.250",
+    "error": "OSM fetch failed for +052.750_+0003.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.000_+0004.250",
+    "error": "OSM fetch failed for +053.000_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.250_+0003.500",
+    "error": "OSM fetch failed for +053.250_+0003.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.250_+0004.500",
+    "error": "OSM fetch failed for +053.250_+0004.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.250_+0004.750",
+    "error": "OSM fetch failed for +053.250_+0004.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.500_+0006.000",
+    "error": "OSM fetch failed for +053.500_+0006.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.500_+0006.250",
+    "error": "OSM fetch failed for +053.500_+0006.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.500_+0006.500",
+    "error": "OSM fetch failed for +053.500_+0006.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.750_+0006.500",
+    "error": "OSM fetch failed for +053.750_+0006.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.750_+0007.250",
+    "error": "OSM fetch failed for +053.750_+0007.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+053.750_+0008.500",
+    "error": "OSM fetch failed for +053.750_+0008.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0004.500",
+    "error": "OSM fetch failed for +054.000_+0004.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0004.750",
+    "error": "OSM fetch failed for +054.000_+0004.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0005.000",
+    "error": "OSM fetch failed for +054.000_+0005.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0008.500",
+    "error": "OSM fetch failed for +054.000_+0008.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0012.000",
+    "error": "OSM fetch failed for +054.000_+0012.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0013.250",
+    "error": "OSM fetch failed for +054.000_+0013.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.000_+0013.750",
+    "error": "OSM fetch failed for +054.000_+0013.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0004.750",
+    "error": "OSM fetch failed for +054.250_+0004.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0007.000",
+    "error": "OSM fetch failed for +054.250_+0007.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0007.250",
+    "error": "OSM fetch failed for +054.250_+0007.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0007.750",
+    "error": "OSM fetch failed for +054.250_+0007.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0011.000",
+    "error": "OSM fetch failed for +054.250_+0011.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0012.500",
+    "error": "OSM fetch failed for +054.250_+0012.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0012.750",
+    "error": "OSM fetch failed for +054.250_+0012.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0015.250",
+    "error": "OSM fetch failed for +054.250_+0015.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0015.500",
+    "error": "OSM fetch failed for +054.250_+0015.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.250_+0015.750",
+    "error": "OSM fetch failed for +054.250_+0015.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0004.750",
+    "error": "OSM fetch failed for +054.500_+0004.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0006.250",
+    "error": "OSM fetch failed for +054.500_+0006.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0007.750",
+    "error": "OSM fetch failed for +054.500_+0007.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0008.000",
+    "error": "OSM fetch failed for +054.500_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0008.750",
+    "error": "OSM fetch failed for +054.500_+0008.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0009.500",
+    "error": "OSM fetch failed for +054.500_+0009.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0009.750",
+    "error": "OSM fetch failed for +054.500_+0009.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0010.000",
+    "error": "OSM fetch failed for +054.500_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0011.000",
+    "error": "OSM fetch failed for +054.500_+0011.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0011.500",
+    "error": "OSM fetch failed for +054.500_+0011.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0011.750",
+    "error": "OSM fetch failed for +054.500_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0012.750",
+    "error": "OSM fetch failed for +054.500_+0012.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0013.000",
+    "error": "OSM fetch failed for +054.500_+0013.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0013.250",
+    "error": "OSM fetch failed for +054.500_+0013.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0014.250",
+    "error": "OSM fetch failed for +054.500_+0014.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.500_+0016.000",
+    "error": "OSM fetch failed for +054.500_+0016.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0005.250",
+    "error": "OSM fetch failed for +054.750_+0005.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0005.500",
+    "error": "OSM fetch failed for +054.750_+0005.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0007.750",
+    "error": "OSM fetch failed for +054.750_+0007.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0009.500",
+    "error": "OSM fetch failed for +054.750_+0009.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0009.750",
+    "error": "OSM fetch failed for +054.750_+0009.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0010.000",
+    "error": "OSM fetch failed for +054.750_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0011.500",
+    "error": "OSM fetch failed for +054.750_+0011.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0011.750",
+    "error": "OSM fetch failed for +054.750_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0012.000",
+    "error": "OSM fetch failed for +054.750_+0012.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0013.750",
+    "error": "OSM fetch failed for +054.750_+0013.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0014.250",
+    "error": "OSM fetch failed for +054.750_+0014.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0014.500",
+    "error": "OSM fetch failed for +054.750_+0014.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+054.750_+0017.500",
+    "error": "OSM fetch failed for +054.750_+0017.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0004.500",
+    "error": "OSM fetch failed for +055.000_+0004.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0006.000",
+    "error": "OSM fetch failed for +055.000_+0006.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0008.000",
+    "error": "OSM fetch failed for +055.000_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0008.250",
+    "error": "OSM fetch failed for +055.000_+0008.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0008.500",
+    "error": "OSM fetch failed for +055.000_+0008.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0010.000",
+    "error": "OSM fetch failed for +055.000_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0010.250",
+    "error": "OSM fetch failed for +055.000_+0010.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0012.000",
+    "error": "OSM fetch failed for +055.000_+0012.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0012.250",
+    "error": "OSM fetch failed for +055.000_+0012.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0012.500",
+    "error": "OSM fetch failed for +055.000_+0012.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0013.250",
+    "error": "OSM fetch failed for +055.000_+0013.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0013.500",
+    "error": "OSM fetch failed for +055.000_+0013.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0013.750",
+    "error": "OSM fetch failed for +055.000_+0013.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0016.500",
+    "error": "OSM fetch failed for +055.000_+0016.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0016.750",
+    "error": "OSM fetch failed for +055.000_+0016.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.000_+0017.000",
+    "error": "OSM fetch failed for +055.000_+0017.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0003.750",
+    "error": "OSM fetch failed for +055.250_+0003.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0004.250",
+    "error": "OSM fetch failed for +055.250_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0006.250",
+    "error": "OSM fetch failed for +055.250_+0006.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0007.000",
+    "error": "OSM fetch failed for +055.250_+0007.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0007.500",
+    "error": "OSM fetch failed for +055.250_+0007.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0008.500",
+    "error": "OSM fetch failed for +055.250_+0008.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0009.500",
+    "error": "OSM fetch failed for +055.250_+0009.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0010.250",
+    "error": "OSM fetch failed for +055.250_+0010.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0010.500",
+    "error": "OSM fetch failed for +055.250_+0010.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0012.000",
+    "error": "OSM fetch failed for +055.250_+0012.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0012.250",
+    "error": "OSM fetch failed for +055.250_+0012.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0013.250",
+    "error": "OSM fetch failed for +055.250_+0013.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.250_+0015.250",
+    "error": "OSM fetch failed for +055.250_+0015.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0006.750",
+    "error": "OSM fetch failed for +055.500_+0006.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0007.750",
+    "error": "OSM fetch failed for +055.500_+0007.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0008.000",
+    "error": "OSM fetch failed for +055.500_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0009.500",
+    "error": "OSM fetch failed for +055.500_+0009.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0011.750",
+    "error": "OSM fetch failed for +055.500_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0012.250",
+    "error": "OSM fetch failed for +055.500_+0012.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.500_+0013.000",
+    "error": "OSM fetch failed for +055.500_+0013.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0003.000",
+    "error": "OSM fetch failed for +055.750_+0003.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0003.500",
+    "error": "OSM fetch failed for +055.750_+0003.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0003.750",
+    "error": "OSM fetch failed for +055.750_+0003.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0005.750",
+    "error": "OSM fetch failed for +055.750_+0005.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0008.000",
+    "error": "OSM fetch failed for +055.750_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0009.750",
+    "error": "OSM fetch failed for +055.750_+0009.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0010.000",
+    "error": "OSM fetch failed for +055.750_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0011.250",
+    "error": "OSM fetch failed for +055.750_+0011.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0011.750",
+    "error": "OSM fetch failed for +055.750_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0012.000",
+    "error": "OSM fetch failed for +055.750_+0012.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0012.750",
+    "error": "OSM fetch failed for +055.750_+0012.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0014.500",
+    "error": "OSM fetch failed for +055.750_+0014.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+055.750_+0014.750",
+    "error": "OSM fetch failed for +055.750_+0014.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0006.000",
+    "error": "OSM fetch failed for +056.000_+0006.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0008.000",
+    "error": "OSM fetch failed for +056.000_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0010.000",
+    "error": "OSM fetch failed for +056.000_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0011.500",
+    "error": "OSM fetch failed for +056.000_+0011.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0012.250",
+    "error": "OSM fetch failed for +056.000_+0012.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0012.500",
+    "error": "OSM fetch failed for +056.000_+0012.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0015.000",
+    "error": "OSM fetch failed for +056.000_+0015.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0017.500",
+    "error": "OSM fetch failed for +056.000_+0017.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.000_+0018.000",
+    "error": "OSM fetch failed for +056.000_+0018.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0003.500",
+    "error": "OSM fetch failed for +056.250_+0003.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0004.250",
+    "error": "OSM fetch failed for +056.250_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0005.500",
+    "error": "OSM fetch failed for +056.250_+0005.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0007.750",
+    "error": "OSM fetch failed for +056.250_+0007.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0008.000",
+    "error": "OSM fetch failed for +056.250_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0010.250",
+    "error": "OSM fetch failed for +056.250_+0010.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0011.750",
+    "error": "OSM fetch failed for +056.250_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.250_+0019.000",
+    "error": "OSM fetch failed for +056.250_+0019.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.500_+0004.750",
+    "error": "OSM fetch failed for +056.500_+0004.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.500_+0005.000",
+    "error": "OSM fetch failed for +056.500_+0005.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.500_+0008.000",
+    "error": "OSM fetch failed for +056.500_+0008.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.500_+0008.250",
+    "error": "OSM fetch failed for +056.500_+0008.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.500_+0018.250",
+    "error": "OSM fetch failed for +056.500_+0018.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.500_+0019.000",
+    "error": "OSM fetch failed for +056.500_+0019.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0004.250",
+    "error": "OSM fetch failed for +056.750_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0008.500",
+    "error": "OSM fetch failed for +056.750_+0008.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0009.250",
+    "error": "OSM fetch failed for +056.750_+0009.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0010.250",
+    "error": "OSM fetch failed for +056.750_+0010.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0011.500",
+    "error": "OSM fetch failed for +056.750_+0011.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0011.750",
+    "error": "OSM fetch failed for +056.750_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+056.750_+0012.250",
+    "error": "OSM fetch failed for +056.750_+0012.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0003.750",
+    "error": "OSM fetch failed for +057.000_+0003.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0006.000",
+    "error": "OSM fetch failed for +057.000_+0006.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0006.250",
+    "error": "OSM fetch failed for +057.000_+0006.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0009.000",
+    "error": "OSM fetch failed for +057.000_+0009.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0009.250",
+    "error": "OSM fetch failed for +057.000_+0009.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0009.500",
+    "error": "OSM fetch failed for +057.000_+0009.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0009.750",
+    "error": "OSM fetch failed for +057.000_+0009.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0011.250",
+    "error": "OSM fetch failed for +057.000_+0011.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0011.500",
+    "error": "OSM fetch failed for +057.000_+0011.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0011.750",
+    "error": "OSM fetch failed for +057.000_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0012.000",
+    "error": "OSM fetch failed for +057.000_+0012.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.000_+0019.500",
+    "error": "OSM fetch failed for +057.000_+0019.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.250_+0004.000",
+    "error": "OSM fetch failed for +057.250_+0004.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.250_+0004.250",
+    "error": "OSM fetch failed for +057.250_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.250_+0006.250",
+    "error": "OSM fetch failed for +057.250_+0006.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.250_+0011.750",
+    "error": "OSM fetch failed for +057.250_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0003.750",
+    "error": "OSM fetch failed for +057.500_+0003.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0004.250",
+    "error": "OSM fetch failed for +057.500_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0007.000",
+    "error": "OSM fetch failed for +057.500_+0007.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0007.500",
+    "error": "OSM fetch failed for +057.500_+0007.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0010.250",
+    "error": "OSM fetch failed for +057.500_+0010.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0014.750",
+    "error": "OSM fetch failed for +057.500_+0014.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0017.500",
+    "error": "OSM fetch failed for +057.500_+0017.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0017.750",
+    "error": "OSM fetch failed for +057.500_+0017.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.500_+0018.000",
+    "error": "OSM fetch failed for +057.500_+0018.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.750_+0004.500",
+    "error": "OSM fetch failed for +057.750_+0004.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.750_+0010.500",
+    "error": "OSM fetch failed for +057.750_+0010.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.750_+0011.750",
+    "error": "OSM fetch failed for +057.750_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+057.750_+0017.500",
+    "error": "OSM fetch failed for +057.750_+0017.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0004.250",
+    "error": "OSM fetch failed for +058.000_+0004.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0005.500",
+    "error": "OSM fetch failed for +058.000_+0005.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0006.500",
+    "error": "OSM fetch failed for +058.000_+0006.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0006.750",
+    "error": "OSM fetch failed for +058.000_+0006.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0007.000",
+    "error": "OSM fetch failed for +058.000_+0007.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0007.750",
+    "error": "OSM fetch failed for +058.000_+0007.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0010.000",
+    "error": "OSM fetch failed for +058.000_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0010.250",
+    "error": "OSM fetch failed for +058.000_+0010.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0011.250",
+    "error": "OSM fetch failed for +058.000_+0011.250: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.000_+0011.750",
+    "error": "OSM fetch failed for +058.000_+0011.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.250_+0010.750",
+    "error": "OSM fetch failed for +058.250_+0010.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.250_+0011.000",
+    "error": "OSM fetch failed for +058.250_+0011.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.500_+0008.750",
+    "error": "OSM fetch failed for +058.500_+0008.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.500_+0010.500",
+    "error": "OSM fetch failed for +058.500_+0010.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.500_+0010.750",
+    "error": "OSM fetch failed for +058.500_+0010.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.500_+0011.000",
+    "error": "OSM fetch failed for +058.500_+0011.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+058.750_+0010.750",
+    "error": "OSM fetch failed for +058.750_+0010.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+059.000_+0002.000",
+    "error": "OSM fetch failed for +059.000_+0002.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+059.000_+0010.000",
+    "error": "OSM fetch failed for +059.000_+0010.000: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+059.000_+0010.750",
+    "error": "OSM fetch failed for +059.000_+0010.750: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+059.250_+0010.500",
+    "error": "OSM fetch failed for +059.250_+0010.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  },
+  {
+    "tile_id": "+059.750_+0010.500",
+    "error": "OSM fetch failed for +059.750_+0010.500: 406 Client Error: Not Acceptable for url: https://overpass-api.de/api/interpreter"
+  }
+]

track_a_short-term_Cross-domain_Datasets/dma_track_v1/context_v1/environment/feature_stats.json

@@ -0,0 +1,32 @@
+{
+  "descriptor_columns": [
+    "patch_radius_m",
+    "grid_size",
+    "nearest_shore_dist_m",
+    "nearest_manmade_dist_m",
+    "nearest_barrier_dist_m",
+    "center_signed_shore_m",
+    "water_ratio",
+    "navigable_ratio",
+    "barrier_density",
+    "natural_boundary_density",
+    "manmade_boundary_density",
+    "natural_boundary_length_m",
+    "manmade_boundary_length_m",
+    "has_natural_boundary",
+    "has_manmade_boundary",
+    "anchor_in_water",
+    "anchor_in_navigable",
+    "anchor_on_barrier",
+    "env_quality_score",
+    "scene_open_water",
+    "scene_nearshore",
+    "scene_harbor",
+    "scene_constrained"
+  ],
+  "log1p_max": {
+    "barrier_density": 0.18314538402176406,
+    "manmade_boundary_density": 0.06440851268683877,
+    "natural_boundary_density": 0.18105909355582347
+  }
+}