| --- |
| 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 |
| dataset_info: |
| - config_name: segmentation |
| features: |
| - name: array_id |
| dtype: string |
| - name: tile_bbox |
| struct: |
| - name: minx |
| dtype: float64 |
| - name: miny |
| dtype: float64 |
| - name: maxx |
| dtype: float64 |
| - name: maxy |
| dtype: float64 |
| - name: tile_size_m |
| dtype: float64 |
| - name: image_source |
| dtype: string |
| - name: insee |
| dtype: string |
| - name: dpt |
| dtype: string |
| - name: kWp |
| dtype: float64 |
| - name: power_class |
| dtype: string |
| - name: n_sources |
| dtype: int64 |
| - name: sources_list |
| list: string |
| - name: centroid_lat |
| dtype: float64 |
| - name: centroid_lon |
| dtype: float64 |
| - name: mask_source |
| dtype: string |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| splits: |
| - name: train |
| num_examples: 435257 |
| --- |
| |
| # 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. |
| |
