{
  "annotation_id": "00dafb22-3938-41aa-857a-d393a986b58f",
  "date_created": "2026-02-17T05:53:12.052000Z",
  "date_modified": "2026-02-17T05:53:12.052000Z",
  "file_hash": "eebe8dc4916466c999a83a2e92f9ecda7da82d46b78e48081591a05a1bc3512e",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.07392",
    "authors": [
      "Alexandre Tuel",
      "Thomas Kerdreux",
      "Quentin Febvre",
      "Alexis Mouche",
      "Antoine Grouazel",
      "Jean-Renaud Miadana",
      "Antoine Audras",
      "Chen Wang",
      "Bertrand Chapron"
    ],
    "categories": [
      "cs.LG",
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "title": "OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation",
    "url": "https://arxiv.org/abs/2601.07392",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "917df72b-338f-4be6-a61e-bd53f573e30f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}