galeio-research

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 - Deployed by: Antoine Audras @ Galeio - 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 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 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 | - 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 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 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 - Same as base model - Minimal additional computational cost for inference - PyTorch >= 1.8.0 - Transformers >= 4.30.0 - Base OceanSAR-1 model - Same as base OceanSAR-1 model - Single channel (VV polarization) SAR images - 256x256 pixel resolution This work was granted access to the HPC resources of IDRIS and TGCC under the allocation 2025-[A0171015666] made by GENCI.