Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - semantic-segmentation
+  - building-detection
+  - remote-sensing
+  - aerial-imagery
+  - geospatial
+size_categories:
+  - 1K<n<10K
+---
+
+# WHU Building Dataset
+
+The WHU Building Dataset is a widely-used benchmark for building extraction from high-resolution aerial imagery. It contains aerial images at **0.3m resolution** with pixel-level binary building masks.
+
+## Dataset Description
+
+| Property | Value |
+|----------|-------|
+| **Resolution** | 0.3m ground sampling distance |
+| **Tile Size** | 512 x 512 pixels |
+| **Channels** | 3 (RGB) |
+| **Classes** | 2 (Background=0, Building=255) |
+| **Format** | PNG |
+
+### Splits
+
+| Split | Images | Masks |
+|-------|--------|-------|
+| Train | 5,732 | 5,732 |
+| Val | 1,228 | 1,228 |
+| Test | 1,228 | 1,228 |
+| **Total** | **8,188** | **8,188** |
+
+## Directory Structure
+
+```
+├── train/
+│   ├── Image/      # 5,732 RGB PNG images (512x512)
+│   └── Mask/       # 5,732 binary mask PNG images (0=background, 255=building)
+├── val/
+│   ├── Image/      # 1,228 RGB PNG images
+│   └── Mask/       # 1,228 binary mask PNG images
+└── test/
+    ├── Image/      # 1,228 RGB PNG images
+    └── Mask/       # 1,228 binary mask PNG images
+```
+
+## Usage
+
+### With GeoAI
+
+```python
+import geoai
+
+# Download and prepare the dataset (converts PNG to GeoTIFF, remaps labels 255→1)
+# See: https://github.com/opengeos/geoai/blob/main/scripts/train_whu_building.py
+```
+
+### Direct Download
+
+```python
+from huggingface_hub import snapshot_download
+
+path = snapshot_download(repo_id="giswqs/WHU-Building-Dataset", repo_type="dataset")
+```
+
+### Pre-trained Model
+
+A pre-trained EfficientNet-B4 + UNet++ model achieving **0.9054 IoU** on the test split is available at:
+[giswqs/whu-building-unetplusplus-efficientnet-b4](https://huggingface.co/giswqs/whu-building-unetplusplus-efficientnet-b4)
+
+## Label Format
+
+- **Background**: pixel value 0
+- **Building**: pixel value 255
+
+> **Note**: When training with CrossEntropyLoss, remap labels from 0/255 to 0/1.
+
+## Citation
+
+```bibtex
+@article{ji2019fully,
+  title={Fully convolutional networks for multisource building identification},
+  author={Ji, Shunping and Wei, Shiqing and Lu, Meng},
+  journal={IEEE Transactions on Geoscience and Remote Sensing},
+  volume={57},
+  number={1},
+  pages={108--120},
+  year={2019},
+  publisher={IEEE}
+}
+```
+
+## References
+
+- Original dataset: [WHU Building Dataset](https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html)
+- GeoAI package: [https://github.com/opengeos/geoai](https://github.com/opengeos/geoai)