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README.md

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 ---
 license: cc-by-4.0
+language:
+- fr
+- en
+pretty_name: OpenPVMapper
+tags:
+- geospatial
+- solar-energy
+- photovoltaic
+- remote-sensing
+- france
+- image-segmentation
+- earth-observation
+size_categories:
+- 1M<n<10M
+task_categories:
+- image-segmentation
+configs:
+- config_name: full
+  default: true
+  data_files:
+  - split: train
+    path: data/full.parquet
+- config_name: high_confidence
+  data_files:
+  - split: train
+    path: data/high_confidence.parquet
+- config_name: validated
+  data_files:
+  - split: train
+    path: data/validated.parquet
+- config_name: segmentation
+  data_files:
+  - split: train
+    path: data/segmentation/*.parquet
 ---
+
+# OpenPVMapper
+
+OpenPVMapper is an open, multi-source database of rooftop photovoltaic
+installations in mainland France: **1,135,850 installations, ~15.0 GWp of
+estimated installed capacity, covering all 96 mainland départements**. It
+aggregates [DeepPVMapper](https://github.com/gabrielkasmi/deeppvmapper/)
+detections (a deep-learning pipeline run on IGN BD ORTHO aerial imagery),
+OpenStreetMap, [FRPV](https://doi.org/10.57745/BXXYW4) (a per-cadastral-parcel
+rooftop-PV presence probability), and manual corrections, resolved into a
+single geometry per installation via a fixed source hierarchy (manual
+correction > OpenStreetMap > DeepPVMapper > third-party detections).
+
+This Hugging Face release adds two things not in the original data
+release: **derived quality/filtering columns** (corroboration count,
+power class, per-source flags) and an **image segmentation config** —
+IGN aerial image + rooftop PV mask pairs for every installation with
+either multi-source corroboration or manual confirmation, in the spirit
+of [BDAPPV](https://huggingface.co/datasets/gabrielkasmi/bdappv).
+
+See the accompanying paper: Kasmi, G. et al., *"OpenPVMapper"*
+(arXiv:[2607.25153](https://arxiv.org/abs/2607.25153)) for the full
+construction methodology and validation protocol.
+
+## Dataset configs
+
+| Config | Rows | Definition |
+|---|---|---|
+| `full` (default) | 1,135,850 | Every installation in the database, no filtering. |
+| `high_confidence` | 430,946 | `n_sources >= 2` — corroborated by at least 2 independent sources. |
+| `validated` | 26,391 | Manually reviewed (`false_positive` is not null), from the paper's precision/recall annotation campaigns. |
+| `segmentation` | 435,257 | Image + mask pairs, for installations that are either `high_confidence` (`n_sources >= 2`) OR manually confirmed as a true positive (`false_positive == 0`). |
+
+`full`, `high_confidence`, and `validated` are geospatial tables (one row
+per installation, GeoParquet with WKB geometry). `segmentation` is an
+image dataset (one row per installation, with an aerial image and a
+rasterized rooftop PV mask).
+
+```python
+from datasets import load_dataset
+
+# segmentation: the main entry point for most users — image/mask pairs
+# ready for a rooftop PV segmentation model
+seg = load_dataset("gabrielkasmi/openpvmapper", "segmentation", split="train")
+
+seg[0]["image"]  # PIL Image, the IGN aerial tile
+seg[0]["mask"]   # PIL Image, single-channel 0/255 rooftop PV mask
+```
+
+A few practical things you can do with the tabular configs and the
+`array_id` join key:
+
+```python
+high_conf = load_dataset("gabrielkasmi/openpvmapper", "high_confidence", split="train")
+
+# residential-scale installations only (P1: 0-9 kWp) — e.g. to study
+# self-consumption behavior separately from utility-scale rooftops
+residential = high_conf.filter(lambda r: r["power_class"] == "P1")
+
+# all installations in a given département — e.g. for a regional
+# capacity study
+gironde = high_conf.filter(lambda r: r["dpt"] == "33")
+
+# build a segmentation training subset restricted to large installations
+# (P4/P5), by filtering the tabular config first and joining on array_id —
+# cheaper than filtering 435k images/masks directly
+large_ids = set(high_conf.filter(lambda r: r["power_class"] in ("P4", "P5"))["array_id"])
+seg_large = seg.filter(lambda r: r["array_id"] in large_ids)
+
+# quality-weighted analysis: n_sources as a confidence proxy instead of a
+# hard cutoff (recall Validation below: precision goes 71.5% -> 96.9% ->
+# 98.2% as n_sources goes 1 -> 2 -> 3)
+full = load_dataset("gabrielkasmi/openpvmapper", "full", split="train")
+by_confidence = full.to_pandas().groupby("n_sources")["kWp"].sum()
+```
+
+For heavier analytical filtering across the full 1.1M-row table, loading
+the Parquet files directly with pandas/DuckDB/polars will generally be
+faster than `datasets.filter()` with a Python predicate.
+
+## Schema
+
+### `full` / `high_confidence` / `validated`
+
+All three share the same schema — `high_confidence` and `validated` are
+row-filtered subsets of `full`, not separately-shaped tables.
+
+Original fields (from the source database):
+
+| Field | Description |
+|---|---|
+| `array_id` | Unique, persistent installation identifier. |
+| `geometry` | Installation polygon (WKB), resolved per the source hierarchy above. CRS: EPSG:4326. |
+| `insee` | INSEE commune code. |
+| `dpt` | Département code. |
+| `rnb_id` | Building identifier (Référentiel National des Bâtiments), if matched. |
+| `surface` | Polygon surface area, m². |
+| `tilt` | Estimated panel tilt, degrees. |
+| `azimuth` | Estimated panel azimuth, degrees. |
+| `kWp` | Estimated installed capacity. |
+| `sources` | Raw encoded source ids (e.g. `"0,2"`) — decoded into `sources_list` below; kept for traceability. |
+| `frpv_proba` | FRPV per-parcel PV-presence probability (0–1), if available. |
+| `first_seen` / `last_seen` | First / most recent vintage in which the installation is confirmed. |
+| `false_positive` | Manual annotation outcome: `0.0` = confirmed true positive, `1.0` = confirmed false positive, `null` = never manually reviewed (the large majority of rows — absence of review, not confirmation of correctness). |
+| `false_positive_source` | Which annotation campaign produced `false_positive` (`dpvm_precision` / `dpvm_recall`), `null` if never reviewed. |
+
+Derived fields (added for this release):
+
+| Field | Description |
+|---|---|
+| `sources_list` | `sources` decoded into readable names, e.g. `["dpvm", "osm"]`. |
+| `n_sources` | `len(sources_list)` — corroboration count, the strongest available quality proxy (see Validation below). |
+| `has_dpvm`, `has_frpv`, `has_osm`, `has_correction` | Boolean flags for the four named sources. |
+| `power_class` | `P1`–`P5` bucketing of `kWp`: P1 (0–9), P2 (9–36), P3 (36–100), P4 (100–250), P5 (>250). |
+| `bbox` | `{minx, miny, maxx, maxy}` bounding box of `geometry`, lon/lat. |
+| `centroid_lon`, `centroid_lat` | Installation centroid, as plain floats (for quick filtering without a geometry engine). |
+
+### `segmentation`
+
+| Field | Description |
+|---|---|
+| `array_id` | Joins back to the tabular configs above. |
+| `image` | IGN BD ORTHO aerial tile, 400×400px, ~0.2m/px ground sample distance, centered on the installation (or on a random interior point for installations too large to fit the tile at fixed GSD — see Limitations). |
+| `mask` | Single-channel (0/255) rooftop PV mask, rasterized from `geometry`, pixel-aligned with `image`. |
+| `image_source` | Imagery provider. `"ign"` for every row in this release (V1). Reserved for future providers (e.g. Sentinel, SPOT) in a later release — always check this column rather than assuming, if you mix releases. |
+| `mask_source` | Provenance of the polygon rasterized into `mask`: `"osm"` if the installation's `sources_list` includes OSM (a human-traced footprint), else `"auto"` (DeepPVMapper/FRPV/correction-derived automated detection — 413,368 / 435,257 rows, ~95%). Will later also carry `"manual_corrected"` for masks fixed through a planned crowdsourced correction tool — see Limitations. |
+| `tile_bbox` | `{minx, miny, maxx, maxy}` of the fetched tile, lon/lat — lets you re-fetch a sharper/alternate image for the same footprint later. |
+| `tile_size_m` | Ground size of the tile in meters (usually 80m at 0.2m/px × 400px; larger for oversized installations framed differently, see Limitations). |
+| `insee`, `dpt`, `kWp`, `power_class`, `n_sources`, `sources_list`, `centroid_lat`, `centroid_lon` | Passed through from the tabular schema above, for filtering without a join. |
+
+## Validation
+
+Precision was assessed by manual review of 1,862 installations (two
+independent stratified samples: by source combination, and by power
+class). **Global precision, weighted by true stratum population: ~74–75%.**
+Corroboration across sources matters a lot — this is the basis for
+`n_sources` as a quality proxy and for the `high_confidence`/`segmentation`
+config perimeters:
+
+| Corroboration | Precision |
+|---|---|
+| 1 source | 71.5% |
+| 2 sources | 96.9% |
+| 3 sources | 98.2% |
+
+Only the `validated` config (26,391 rows) carries a directly human-checked
+label (`false_positive`). The 1,862-installation precision sample above is
+a separate, smaller stratified audit used to estimate accuracy across the
+whole database — most individual rows outside `validated` have never been
+looked at by a human.
+
+## Limitations
+
+- **Rooftop PV masks are algorithmically generated, not manually
+  annotated.** Every mask in `segmentation` is a rasterization of a
+  polygon produced by the automated multi-source pipeline (DeepPVMapper
+  detection, OpenStreetMap tracing, or manual correction where
+  `mask_source == "osm"`/available) — not a pixel-level human annotation.
+  Overlap with the true panel outline is generally good but can be
+  imperfect or partial, especially for irregular roof shapes or
+  multi-part arrays. **If you need manually annotated, pixel-accurate
+  segmentation masks, use
+  [BDAPPV](https://huggingface.co/datasets/gabrielkasmi/bdappv) instead**,
+  which is purpose-built for that. A crowdsourced mask-correction tool is
+  planned for OpenPVMapper (see `mask_source` above); this card will be
+  updated as corrected masks land.
+- **Installation/polygon boundaries carry inherent ambiguity** — e.g. where
+  a large industrial roof has several separately-tilted PV arrays, or
+  where DeepPVMapper's detection and OSM's tracing disagree on the exact
+  building/array boundary. `geometry` reflects the source hierarchy's
+  resolution, not a single unambiguous ground truth.
+- **Global precision (~74-75%) applies to `full`, not the whole database
+  uniformly** — precision rises sharply with `n_sources` (see Validation
+  above), which is exactly why `high_confidence` and `segmentation` filter
+  on it. Use `full` only if you specifically need recall over precision,
+  or intend to filter/weight by `n_sources` yourself.
+- **Large installations in `segmentation`** (roughly >32m in ground
+  extent) don't fit inside a single fixed-GSD 400×400px tile alongside
+  their full context. Rather than vary the GSD (which would make masks
+  inconsistent in scale across the dataset) or split into sub-tiles, this
+  release centers the tile on a random point inside the installation's
+  polygon (seeded by `array_id`, so reproducible) — the mask may then only
+  partially cover the tile. Check `tile_size_m` if this matters for your
+  use case.
+- **`image_source` is `"ign"` for every row in this release.** The column
+  is reserved for a planned V2 extension (Sentinel/SPOT imagery) — don't
+  assume future releases are IGN-only.
+
+## Attribution & citation
+
+Data licensed **CC-BY 4.0** (code used to build this release is licensed
+separately — see the linked repositories).
+
+- **DeepPVMapper**: detection pipeline. Source:
+  [github.com/gabrielkasmi/deeppvmapper](https://github.com/gabrielkasmi/deeppvmapper/).
+- **OpenStreetMap**: © OpenStreetMap contributors.
+- **FRPV**: Nerot, B.; Thébault, M. (2024). *"FRPV - Presence of Rooftop
+  Photovoltaic (RPV) systems on French buildings."* Recherche Data Gouv,
+  V3. [doi.org/10.57745/BXXYW4](https://doi.org/10.57745/BXXYW4)
+- **Imagery**: © IGN — BD ORTHO, via the Géoplateforme WMS API
+  ([data.geopf.fr](https://data.geopf.fr)).
+- **Paper**: Kasmi, G. et al., arXiv:[2607.25153](https://arxiv.org/abs/2607.25153).
+
+If you use this dataset, please cite the paper above alongside the
+FRPV and OpenStreetMap attributions where relevant.

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