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README.md

@@ -1,117 +1,183 @@
 ---
 license: cc-by-4.0
-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
+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
 ---
+
+# 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)
+
+---
+
+## 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}
+}
+```