README.md

---
license: apache-2.0
base_model:
- galeio-research/OceanSAR-1
pipeline_tag: image-classification
library_name: transformers
tags:
- EO
- SAR
- sentinel-1
- wind
- ocean
- earth-observation
- remote-sensing
- satellite-imagery
- synthetic-aperture-radar
- foundation-model
- linear-probing
- oceanography
- marine-forecasting
- open-source
- ocean-wind
---

# Model Card for OceanSAR-1-Wind

<img src="OceanSAR-1-logo.png" width=400>

## Model Description

OceanSAR-1-Wind is a linear probing head for wind speed prediction built on top of the OceanSAR-1 foundation model. It leverages the powerful features extracted by OceanSAR-1 to accurately predict wind speed from Synthetic Aperture Radar (SAR) imagery.

- **Developed by:** Thomas Kerdreux, Alexandre Tuel @ [Galeio](http://galeio.fr)
- **Deployed by:** Antoine Audras @ [Galeio](http://galeio.fr)
- **Model type:** Linear Regression Head on Vision Foundation Model
- **License:** Apache License 2.0
- **Base model:** OceanSAR-1 (ResNet50/ViT variants)
- **Training data:** Sentinel-1 Wave Mode (WV) SAR images with collocated wind speed measurements

## Uses

### Direct Use

This model is designed for wind speed prediction from SAR imagery, particularly over ocean surfaces. It can be used for:

- Near-real-time wind speed estimation from SAR images
- Assimilation into meteorological models
- Marine weather forecasting
- Offshore operations planning

### Performance Results

The model achieves state-of-the-art linear probing performances on wind speed prediction, with performance varying by backbone architecture:

| Backbone | Wind RMSE (m/s) |
|----------|----------------|
| ResNet50 | 1.62 |
| ViT-S/16 | 1.39 |
| ViT-S/8  | 1.38 |
| ViT-B/8  | 1.37 |

## How to Use

```python
import torch
from transformers import AutoModelForImageClassification

# Load the foundation model and wind prediction head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1-wind")

# Prepare your SAR image (should be single-channel VV polarization)
dummy_image = torch.randn(1, 1, 256, 256)  # (C, H, W)

# Extract features and predict wind speed
with torch.no_grad():
    wind_speed = oceansar(dummy_image).logits  # Output in m/s
```

## Training Details

### Training Data

- **Dataset:** Sentinel-1 Wave Mode (WV) SAR images with collocated wind speed measurements
- **Source:** Wind speed measurements from scatterometer and buoy data
- **Preprocessing:** Same as base OceanSAR-1 model

## Evaluation

### Metrics

Wind speed prediction performance is evaluated using Root Mean Square Error (RMSE), achieving:
- 1.62 m/s RMSE with ResNet50 backbone
- 1.39 m/s RMSE with ViT-S/16 backbone
- 1.38 m/s RMSE with ViT-S/8 backbone
- 1.37 m/s RMSE with ViT-B/8 backbone

### Comparison to Other Backbones

The model outperforms existing approaches:
- MoCo: 1.80 m/s RMSE
- DeCUR: 1.93 m/s RMSE
- SoftCon ViT-S/14: 1.98 m/s RMSE
- SoftCon ViT-B/14: 1.95 m/s RMSE

## Technical Specifications

### Hardware Requirements

- Same as base model
- Minimal additional computational cost for inference

### Dependencies

- PyTorch >= 1.8.0
- Transformers >= 4.30.0
- Base OceanSAR-1 model

### Input Specifications

- Same as base OceanSAR-1 model
- Single channel (VV polarization) SAR images
- 256x256 pixel resolution

## Citation

**BibTeX:**
```bibtex
@article{kerdreux2025efficientselfsupervisedlearningearth,
  title={Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation},
  author={Kerdreux, Thomas and Tuel, Alexandre and Febvre, Quentin and Mouche, Alexis and Chapron, Bertrand},
  journal={arXiv preprint arXiv:2504.06962},
  year={2025},
  eprint={2504.06962},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2504.06962},
}
```

## Acknowledgements

This work was granted access to the HPC resources of IDRIS and TGCC under the allocation 2025-[A0171015666] made by GENCI.