---
license: cc-by-nc-4.0
task_categories:
- image-feature-extraction
size_categories:
- 1M<n<10M
---

# SAR-1M Dataset
## Dataset Description

SAR-1M is a large-scale synthetic aperture radar (SAR) image dataset designed for SAR representation learning. 

The dataset contains over one million SAR images, and about 75% of the SAR samples are paired with geographically aligned optical images, enabling multimodal remote sensing studies.

## Dataset Structure

```
SAR-1M/
├── SAR/
│   ├── 0000001.png
│   ├── 0000002.png
│   └── ...
├── OPT/
│   ├── 0000001.png
│   ├── 0000002.png
│   └── ...
├── paired.json
└── unpaired.json
```

- **SAR/**: SAR imagery  
- **OPT/**: Corresponding optical images (for paired samples)  
- **paired.json**: Index file describing SAR–optical paired samples  
- **unpaired.json**: Index file for SAR samples without optical counterparts  

## Applications

The dataset can support various remote sensing tasks, including:

- SAR image representation learning  
- Multimodal remote sensing research  
- Foundation model pretraining for SAR imagery  

## License

The SAR-1M dataset is released under the **CC BY-NC 4.0** license and is intended for **non-commercial research purposes only**.

## Contact

For questions or collaboration inquiries, please contact the dataset authors.

## Citation

If you use the SAR-1M dataset in your research, please cite:
```
@misc{liu2025sarmaemaskedautoencodersar,
  title={SARMAE: Masked Autoencoder for SAR Representation Learning},
  author={Danxu Liu and Di Wang and Hebaixu Wang and Haoyang Chen and Wentao Jiang and Yilin Cheng and Haonan Guo and Wei Cui and Jing Zhang},
  year={2025},
  eprint={2512.16635},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2512.16635}
}
```