README.md

---
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://gpcv.whu.edu.cn/data/building_dataset.html)
- GeoAI package: [https://github.com/opengeos/geoai](https://github.com/opengeos/geoai)