--- 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 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.