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OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation

arXiv 2601.07392 ↗First posted 12 Jan 2026cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation. Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies, which enhances performance while reducing training cost. OceanSAR-2 demonstrates strong transfer performance across downstream tasks, including geophysical pattern classification, ocean surface wind vector and significant wave height estimation, and iceberg detection. We release standardized benchmark datasets, providing a foundation for systematic evaluation and advancement of SAR models for ocean applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 12 Jan 2026.

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12 of 19 references.

References · 12

  1. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  2. 2020End-to-End Object Detection with Transformerscited by 7,384
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  4. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  5. 2021iBOT: Image BERT Pre-Training with Online Tokenizercited by 891
  6. 2022SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagerycited by 402
  7. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 139
  8. 2025TerraMind: Large-Scale Generative Multimodality for Earth Observationcited by 101
  9. 2022SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observationcited by 82
  10. 2025Towards a Unified Copernicus Foundation Model for Earth Visioncited by 52
  11. 2025TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Datacited by 14
  12. 2025Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curationcited by 7

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