---
license: cc-by-nc-sa-4.0
---
# OSCD100

OSCD100 is a subset of the [Onera Satellite Change Detection (OSCD)](https://rcdaudt.github.io/oscd/) dataset, containing 100 pre-cropped image pairs at 256x256 resolution. It is designed for tutorials and demonstrations, not benchmarking.

## Dataset Structure

The dataset maintains the same file structure and all 13 Sentinel-2 bands as the full OSCD dataset:
- **Bands**: B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, B12
- **Splits**: train (60 samples), val (20 samples), test (20 samples)
- **Format**: GeoTIFF for imagery, PNG for labels
- **Resolution**: 256x256 pixels per crop
- **Temporal**: Each sample contains two images (pre and post change)

## Files

- `Onera Satellite Change Detection dataset - Images.zip` (220MB): All image pairs with 13 Sentinel-2 bands
- `Onera Satellite Change Detection dataset - Train Labels.zip`: Training labels
- `Onera Satellite Change Detection dataset - Val Labels.zip`: Validation labels
- `Onera Satellite Change Detection dataset - Test Labels.zip`: Test labels

## Usage

```python
from torchgeo.datasets import OSCD100

# Load dataset
dataset = OSCD100(root='data', split='train', download=True)

# Get a sample
sample = dataset[0]
print(sample['image'].shape)  # torch.Size([2, 13, 256, 256])
print(sample['mask'].shape)   # torch.Size([1, 256, 256])
```

## Citation

If you use this dataset, please cite the original OSCD paper:

```bibtex
@inproceedings{daudt2018urban,
  title={Urban change detection for multispectral earth observation using convolutional neural networks},
  author={Daudt, Rodrigo Caye and Le Saux, Bertr and Boulch, Alexandre},
  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
  pages={2115--2118},
  year={2018},
  organization={IEEE}
}
```

## License

Same as the original OSCD dataset.