Oceansar 1

🚀 OceanSAR-1

OceanSAR-1 is the first foundation model in the OceanSAR family, designed for Synthetic Aperture Radar (SAR) imagery analysis, especially for ocean observation.

🚀 Quick Start

import torch
from transformers import AutoModel

# Load model and processor
model = AutoModel.from_pretrained("galeio-research/OceanSAR-1")

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

# Extract features
with torch.no_grad():
    outputs = model(dummy_image)
    features = outputs.pooler_output  # Shape: (1, 2048) for ResNet50

✨ Features

• OceanSAR-1 is specifically designed for Synthetic Aperture Radar (SAR) imagery analysis, with a focus on ocean observation.
• It uses a novel dynamic dataset pruning strategy to enhance training efficiency and feature quality.
• The model can be used for feature extraction from SAR images and transfer learning for downstream tasks.
• It has been validated on three downstream tasks: TenGeoP Classification, Significant Wave Height Estimation, and Wind Speed Prediction.

📦 Installation

The dependencies required for this model are as follows:

• PyTorch >= 1.8.0
• Transformers >= 4.30.0
• torchvision >= 0.9.0

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModel

# Load model and processor
model = AutoModel.from_pretrained("galeio-research/OceanSAR-1")

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

# Extract features
with torch.no_grad():
    outputs = model(dummy_image)
    features = outputs.pooler_output  # Shape: (1, 2048) for ResNet50

📚 Documentation

Model Details

Model Description

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.

Uses

Direct Use

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:

• Feature extraction from SAR images
• Transfer learning for downstream tasks

Downstream Use

The model has been validated on three downstream tasks:

1. TenGeoP Classification: Classification of 10 geophysical phenomena in SAR images
2. Significant Wave Height Estimation: Regression task for ocean wave height prediction
3. Wind Speed Prediction: Regression task for surface wind speed estimation

Training Details

Training Data

• Dataset: Sentinel-1 Wave Mode (WV) SAR images
• Time period: 2015 - 2024
• Size: ~12 million images
• Preprocessing:
  • Spatial downsampling to 50m resolution
  • Dynamic dataset pruning for diversity and balancedness
  • Excluded validation images from training set

Dynamic Dataset Pruning

The model uses a novel dynamic dataset pruning strategy that:

• Maximizes dataset diversity and balancedness
• Reduces computational costs
• Improves model performance on downstream tasks
• Works without requiring a pre-existing feature extractor

Evaluation

Results

The model achieves state-of-the-art performance on three downstream tasks (linear probing):

1. TenGeoP Classification:
   • ResNet50: 75.5% accuracy
   • ViT-S/16: 78.6% accuracy
   • ViT-S/8: 82.1% accuracy
   • ViT-B/8: 83.6% accuracy
2. Significant Wave Height Estimation:
   • RMSE: 0.63 - 0.72m (depending on architecture)
3. Wind Speed Prediction:
   • RMSE: 1.37 - 1.43 m/s (depending on architecture)

Technical Specifications

Hardware Requirements

• GPU with at least 8GB VRAM recommended

Dependencies

• PyTorch >= 1.8.0
• Transformers >= 4.30.0
• torchvision >= 0.9.0

Input Specifications

• Input size: 256x256 pixels
• Single channel (VV polarization)
• Normalized pixel values
• SAR images from Sentinel-1 Wave Mode

Citation

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.

📄 License

This model is licensed under the Apache License 2.0.