README.md

---
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.