Datasets:

gabrielkasmi
/

bdappv

license: cc-by-4.0
task_categories:
  - image-segmentation
  - image-classification
language: []
tags:
  - solar-panels
  - photovoltaic
  - remote-sensing
  - aerial-imagery
  - segmentation
  - distribution-shift
  - france
  - belgium
pretty_name: BDAPPV
size_categories:
  - 10K<n<100K
configs:
  - config_name: google
    data_files:
      - split: train
        path: google/train-*
      - split: validation
        path: google/validation-*
      - split: test
        path: google/test-*
  - config_name: ign
    data_files:
      - split: train
        path: ign/train-*
      - split: validation
        path: ign/validation-*
      - split: test
        path: ign/test-*
dataset_info:
  - config_name: google
    features:
      - name: identifiant
        dtype: string
      - name: image
        dtype: image
      - name: mask
        dtype: image
      - name: has_mask
        dtype: bool
      - name: split
        dtype: string
      - name: surface
        dtype: float32
      - name: azimuth
        dtype: float32
      - name: tilt
        dtype: float32
      - name: kWp
        dtype: float32
      - name: departement
        dtype: int32
      - name: city
        dtype: string
      - name: dateInstalled
        dtype: string
      - name: typeInstallation
        dtype: int32
      - name: countArrays
        dtype: int32
      - name: countInverters
        dtype: int32
      - name: isIntegrated
        dtype: bool
      - name: selfConsumption
        dtype: bool
    splits:
      - name: train
        num_bytes: 2447133010
        num_examples: 20707
      - name: validation
        num_bytes: 441348728
        num_examples: 3817
      - name: test
        num_bytes: 451607704
        num_examples: 3884
    download_size: 3341481346
    dataset_size: 3340089442
  - config_name: ign
    features:
      - name: identifiant
        dtype: string
      - name: image
        dtype: image
      - name: mask
        dtype: image
      - name: has_mask
        dtype: bool
      - name: split
        dtype: string
      - name: surface
        dtype: float32
      - name: azimuth
        dtype: float32
      - name: tilt
        dtype: float32
      - name: kWp
        dtype: float32
      - name: departement
        dtype: int32
      - name: city
        dtype: string
      - name: dateInstalled
        dtype: string
      - name: typeInstallation
        dtype: int32
      - name: countArrays
        dtype: int32
      - name: countInverters
        dtype: int32
      - name: isIntegrated
        dtype: bool
      - name: selfConsumption
        dtype: bool
    splits:
      - name: train
        num_bytes: 3204106988
        num_examples: 11526
      - name: validation
        num_bytes: 875106431
        num_examples: 3206
      - name: test
        num_bytes: 694527761
        num_examples: 2593
    download_size: 4783574371
    dataset_size: 4773741180

BDAPPV — Aerial Images of Rooftop Photovoltaic Installations

BDAPPV is a dataset of aerial images of rooftop PV installations in France and Belgium, with segmentation masks and installation metadata. Images are provided by two aerial imagery providers (Google and IGN), making it suitable for both segmentation/classification benchmarks and distribution shift evaluation across imagery sources.

Paper: Kasmi et al., Scientific Data, 2023 — arXiv:2209.03726

Dataset overview

Provider	Images	Positifs (masks)	Négatifs	Note
Google	28,408	13,303	15,105	399 images excluded (no metadata entry)
IGN	17,325	7,685	9,640

Images are 400×400 px PNG files.
Google images are a superset: every IGN installation also has a Google image.
Masks are binary PNGs (same resolution as images).

Data structure

bdappv/
├── google/
│   ├── img/    # 28,408 images (28,807 raw − 399 excluded)
│   └── mask/   # 13,303 segmentation masks
├── ign/
│   ├── img/    # 17,325 images
│   └── mask/   #  7,685 segmentation masks
├── annotations.csv   # manifest: one row per (installation × provider)
├── metadata.csv      # installation-level metadata
└── README.md

Loading the dataset

from datasets import load_dataset

# Google imagery (default)
ds = load_dataset("gabrielkasmi/bdappv", "google")

# IGN imagery
ds = load_dataset("gabrielkasmi/bdappv", "ign")

Each example contains:

{
    "identifiant": "OSIBG1RDEDJ",   # installation ID
    "image":       <PIL Image>,      # 400×400 aerial image
    "mask":        <PIL Image>,      # segmentation mask (None if has_mask=False)
    "has_mask":    True,             # False = negative sample (no panel)
    "split":       "train",          # train / val / test
    "surface":     22.0,             # panel surface (m²)
    "azimuth":     -20.0,            # panel azimuth (degrees)
    "tilt":        20.0,             # panel tilt (degrees)
    "kWp":         3010.0,           # peak power (Wp)
    "departement": 31,               # French department code
    "city":        "Castanet-Tolosan",
    "dateInstalled": "2007-09-01",
    ...
}

Recommended usage patterns

Segmentation (positives only)

ds = load_dataset("gabrielkasmi/bdappv", "google")
train_seg = ds["train"].filter(lambda x: x["has_mask"])
# 13,303 images with masks across all splits

Binary classification (panel / no panel)

# Both providers have validated negatives
ds_google = load_dataset("gabrielkasmi/bdappv", "google")  # 13,303 pos / 15,105 neg
ds_ign    = load_dataset("gabrielkasmi/bdappv", "ign")      #  7,685 pos /  9,640 neg
# has_mask is the binary label (True = panel present)

Distribution shift benchmark (cross-provider)

The intended protocol for evaluating robustness to imagery distribution shift:

train = load_dataset("gabrielkasmi/bdappv", "google", split="train")
test  = load_dataset("gabrielkasmi/bdappv", "ign",    split="test")
# Train on Google, evaluate on IGN — same installations, different sensors

Note: pooling both providers for training is not recommended as a default setup. Google and IGN images of the same installation share the same ground truth object; pooling them amounts to domain augmentation rather than independent data, and conflates the distribution shift signal. If you want to pool, build a custom dataloader merging both configs.

Train / val / test split

Split is based on spatial holdout by French department to prevent geographic leakage between splits. All Belgian and small-department installations are assigned to train.

Split	Installations	Departments
train	20,707 (73%)	all others
val	3,817 (13%)	3, 9, 11, 23, 44, 47, 52, 54, 59, 66, 72, 82, 88, 92
test	3,884 (14%)	2, 4, 6, 15, 16, 32, 38, 42, 51, 64, 67, 85, 91

The split is fixed and deterministic (seed=42). Do not re-split to ensure comparability with published results.

Licenses

This dataset combines components under different licenses:

Component	License
Segmentation masks & annotations	CC-BY 4.0
Installation metadata	CC-BY 4.0
Google aerial images	Google Earth Engine ToS — non-commercial use only
IGN aerial images	Etalab Open License 2.0 — free incl. commercial use

Important: the Google imagery restricts commercial use. For commercial applications, use the IGN configuration only (load_dataset("gabrielkasmi/bdappv", "ign")).

Citation

@article{kasmi2023bdappv,
  title     = {A crowdsourced dataset of aerial images with annotated solar
               photovoltaic arrays and installation metadata},
  author    = {Kasmi, Gabriel and Saint-Drenan, Yves-Marie and Trebosc, David
               and Jolivet, Rapha{\"e}l and Leloux, Jonathan and Sarr, Babacar
               and Dubus, Laurent},
  journal   = {Scientific Data},
  volume    = {10},
  number    = {1},
  pages     = {59},
  year      = {2023},
  publisher = {Nature Publishing Group},
  doi       = {10.1038/s41597-023-01951-4}
}