{
  "abstract": "High-resolution SAR time-series datasets remain scarce for high-mountain land cover applications, limiting the development of advanced machine learning methods for temporal monitoring of mountain surfaces in complex Alpine terrain. This paper introduces CPAZMaL: Cryosphere analysis with PAZ images for Machine Learning, a comprehensive benchmark dataset combining 3 years of X-band SAR acquisitions from PAZ satellites over the Mont-Blanc massif (Western European Alps). The dataset encompasses 8 terrain classes, including rarely studied glacier types such as ice aprons and hanging glaciers alongside well-characterized accumulation and ablation zones, as well as non-glacial surfaces such as rock outcrops, Alpine grasslands, and forested areas, enabling the study of landscape transitions driven by climate change. All the SAR images were processed through a standardized radiometric calibration and terrain correction workflow using a custom 1.5-m LiDAR DEM, ensuring geometric and radiometric consistency across dualpolarization (HH/HV) and both orbit (ascending/descending) configurations. Data are organized in a hierarchical HDF5 structure with group-based sampling to minimize spatial autocorrelation. We establish baseline performance across 4 machine learning scenarios: temporal classification, time-series forecasting, and domain adaptation, addressing polarimetric and cross-sensor transfer. Results demonstrate the dataset's utility for developing robust classification methods that handle geometric distortions, temporal variability, and the limited training samples inherent to high-resolution SAR data in steep Alpine environments, with a strong emphasis on glacial dynamics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Matthieu Gallet"
    },
    {
      "name": "Suvrat Kaushik"
    },
    {
      "name": "Christophe Lin-Kwong-Chon"
    },
    {
      "name": "Ludovic Ravanel"
    },
    {
      "name": "Emmanuel Trouvé"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1t3pvxvpyd825.bib",
  "cited_by": 0,
  "doi": "10.1109/jstars.2026.3713622",
  "fulltext_url": null,
  "id": "1t3pvxvpyd825",
  "issns": [
    "1939-1404"
  ],
  "landing_url": "https://hal.science/hal-05743098v1",
  "language": "en",
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "0.info"
    ],
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    [
      "openalex",
      "2202|Synthetic Aperture Radar (SAR) Applications and Techniques"
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  "openalex": "W7168366036",
  "pdf_url": "https://hal.science/hal-05743098/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-15",
  "publisher": "IEEE",
  "ris": "https://research.draconitic.com/paper/1t3pvxvpyd825.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "hal-05743098",
      "source": "hal",
      "url": "https://hal.science/hal-05743098v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:0.sde",
      "subfield": "environmental-science"
    },
    {
      "field": "earth",
      "origin": "hal:2.sdu.stu.gl",
      "subfield": "oceanography"
    }
  ],
  "title": "CPAZMaL: A New Dataset of X-band SAR Time Series in the French Alps for Machine Learning",
  "title_text": "CPAZMaL: A New Dataset of X-band SAR Time Series in the French Alps for Machine Learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/1t3pvxvpyd825",
  "venue": "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
  "versions": []
}