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--- |
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library_name: transformers |
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tags: |
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- resnet |
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- SAR |
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- RADAR |
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- EO |
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- backbone |
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- ocean |
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- wind |
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- sentinel-1 |
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license: apache-2.0 |
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pipeline_tag: image-feature-extraction |
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--- |
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# Model Card for OceanSAR-1 |
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## Model Details |
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<img src="OceanSAR-1-logo.png" width=400> |
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### Model Description |
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OceanSAR-1 is the first foundation model in the OceanSAR family, specifically designed for Synthetic Aperture Radar (SAR) imagery analysis, with a focus on ocean observation. The model is trained using a novel dynamic dataset pruning strategy that enhances training efficiency and feature quality. |
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- **Developed by:** Thomas Kerdreux, Alexandre Tuel @ [Galeio](http://galeio.fr) |
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- **Deployed by:** Antoine Audras @ [Galeio](http://galeio.fr) |
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- **Model type:** Vision Foundation Model (ResNet50/ViT variants) |
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- **License:** Apache License 2.0 |
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- **Training data:** Sentinel-1 Wave Mode (WV) SAR images (2015-2024) |
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- **Training regime:** DINO self-supervised learning with dynamic dataset pruning |
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## Uses |
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### Direct Use |
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The model is intended to be used as a feature extractor for SAR image analysis, particularly for ocean observation tasks. It can be used for: |
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- Feature extraction from SAR images |
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- Transfer learning for downstream tasks |
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### Downstream Use |
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The model has been validated on three downstream tasks: |
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1. **TenGeoP Classification**: Classification of 10 geophysical phenomena in SAR images |
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2. **Significant Wave Height Estimation**: Regression task for ocean wave height prediction |
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3. **Wind Speed Prediction**: Regression task for surface wind speed estimation |
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## How to Use |
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```python |
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import torch |
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from transformers import AutoModel |
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# Load model and processor |
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model = AutoModel.from_pretrained("galeio-research/OceanSAR-1") |
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# Prepare your SAR image (should be single-channel VV polarization) |
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# Here using random data as example |
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dummy_image = torch.randn(1, 1, 256, 256) # (C, H, W) |
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# Extract features |
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with torch.no_grad(): |
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outputs = model(dummy_image) |
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features = outputs.pooler_output # Shape: (1, 2048) for ResNet50 |
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``` |
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## Training Details |
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### Training Data |
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- **Dataset:** Sentinel-1 Wave Mode (WV) SAR images |
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- **Time period:** 2015-2024 |
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- **Size:** ~12 million images |
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- **Preprocessing:** |
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- Spatial downsampling to 50m resolution |
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- Dynamic dataset pruning for diversity and balancedness |
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- Excluded validation images from training set |
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### Dynamic Dataset Pruning |
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The model uses a novel dynamic dataset pruning strategy that: |
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- Maximizes dataset diversity and balancedness |
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- Reduces computational costs |
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- Improves model performance on downstream tasks |
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- Works without requiring a pre-existing feature extractor |
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## Evaluation |
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### Results |
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The model achieves state-of-the-art performance on three downstream tasks (linear probing): |
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1. **TenGeoP Classification**: |
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- ResNet50: 75.5% accuracy |
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- ViT-S/16: 78.6% accuracy |
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- ViT-S/8: 82.1% accuracy |
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- ViT-B/8: 83.6% accuracy |
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2. **Significant Wave Height Estimation**: |
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- RMSE: 0.63-0.72m (depending on architecture) |
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3. **Wind Speed Prediction**: |
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- RMSE: 1.37-1.43 m/s (depending on architecture) |
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For commercial deployments or to access optimized model variants for specific operational needs, feel free to reach out to discuss licensing and support options. |
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## Technical Specifications |
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### Hardware Requirements |
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- GPU with at least 8GB VRAM recommended |
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### Dependencies |
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- PyTorch >= 1.8.0 |
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- Transformers >= 4.30.0 |
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- torchvision >= 0.9.0 |
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### Input Specifications |
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- Input size: 256x256 pixels |
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- Single channel (VV polarization) |
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- Normalized pixel values |
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- SAR images from Sentinel-1 Wave Mode |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@article{kerdreux2025efficientselfsupervisedlearningearth, |
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title={Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation}, |
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author={Kerdreux, Thomas and Tuel, Alexandre and Febvre, Quentin and Mouche, Alexis and Chapron, Bertrand}, |
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journal={arXiv preprint arXiv:2504.06962}, |
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year={2025}, |
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eprint={2504.06962}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2504.06962}, |
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} |
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``` |
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## Acknowledgements |
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This work was granted access to the HPC resources of IDRIS and TGCC under the allocation 2025-[A0171015666] made by GENCI. |
