README.md

---
license: cc-by-4.0
language: [en]
pretty_name: "MARIS-Forecast: a multi-region AIS trajectory dataset with temporally aligned maritime context for vessel trajectory forecasting"
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
---

**Persistent identifier**: [10.5281/zenodo.21224009](https://doi.org/10.5281/zenodo.21224009) (Zenodo record, mirrors this HF release).

**Companion paper**: Ma, K. (in prep.). *MARIS-Forecast: a multi-region AIS trajectory dataset with temporally aligned maritime context for vessel trajectory forecasting.* Scientific Data.

# MARIS-Forecast — Multi-Region AIS Trajectory Dataset for Vessel Trajectory Forecasting

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{ma_maris_forecast_2026,
  author    = {Ma, Kun},
  title     = {MARIS-Forecast: a multi-region AIS trajectory dataset with temporally aligned maritime context for vessel trajectory forecasting},
  year      = 2026,
  publisher = {Zenodo},
  version   = {v1.0.0},
  doi       = {10.5281/zenodo.21224009},
  url       = {https://doi.org/10.5281/zenodo.21224009}
}
```

Mirror on Hugging Face (bit-for-bit identical files):
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.