| --- |
| 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](https://doi.org/10.1038/s41597-023-01951-4) — [arXiv:2209.03726](https://arxiv.org/abs/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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| { |
| "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) |
|
|
| ```python |
| 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) |
|
|
| ```python |
| # 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: |
|
|
| ```python |
| 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](https://creativecommons.org/licenses/by/4.0/) | |
| | Installation metadata | [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) | |
| | Google aerial images | [Google Earth Engine ToS](https://cloud.google.com/maps-platform/terms) — **non-commercial use only** | |
| | IGN aerial images | [Etalab Open License 2.0](https://www.etalab.gouv.fr/licence-ouverte-open-licence/) — 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
