{
  "abstract": "Ice shelves buttress the discharge of Antarctica’s outlet glaciers, mitigating their contribution to sea-level rise. Yet progressive damage through fracturing remains largely absent from projections, partly because long-term observations of ice-shelf fractures remain limited despite their critical influence on ice-shelf stability. Here, we introduce BASIS (BAsal and Surface Ice-shelf fracture extraction System), a deep-learning model for mapping surface crevasses and basal-fracture across Antarctic ice shelves. Unlike existing approaches based on classical edge detectors or models trained on edge-based datasets, BASIS is designed to delineate the full extent of fracture features, including diffuse basal-fracture signatures and highly damaged, chaotic regions. We train, validate, and test BASIS, based on DeepLabv3, using a unique manually labeled fracture dataset spanning 14 Antarctic ice shelves and the full Landsat observation era. Model performance approaches inter-annotator agreement, while maintaining the distinction between basal and surface fractures. We quantify biases associated with sensor transitions and observation availability and develop a framework for producing consistent multi-sensor fracture records. Annual mosaics reveal the spatiotemporal evolution of ice-shelf damage from the 1980s onward and show that basal-fracture signatures dominate the mapped damage patterns across the ice shelves examined here. At Pine Island Glacier Ice Shelf, BASIS resolves successive stages of shear-margin damage together with a progressive increase in basal-fracture. At Mertz and Borchgrevink ice shelves, it reveals extensive networks of basal-fracture signatures that are poorly represented in existing continent-wide products based on Sentinel-1 SAR imagery. Finally, BASIS demonstrates promising cross-sensor transferability to Sentinel-2 and ASTER optical imagery and, despite having been trained exclusively on Landsat data, also recovers coherent fracture patterns from SAR imagery. These results provide a foundation for large-scale, multi-sensor monitoring of Antarctic ice-shelf damage across unprecedented spatial and temporal scales.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Kaian Shahateet"
    },
    {
      "name": "Colin Prieur"
    },
    {
      "name": "Romain Millan"
    },
    {
      "name": "Lucille Gimenes"
    },
    {
      "name": "Andrea Sofie Haastrup-Vang"
    },
    {
      "name": "Jaime Roas"
    },
    {
      "name": "Maaike Izeboud"
    },
    {
      "name": "Raphaelle Charrassin"
    },
    {
      "name": "Emil Giraldi"
    },
    {
      "name": "Romain Santiard"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fck3c7wb4522h.bib",
  "cited_by": 0,
  "doi": "10.5194/egusphere-2026-5765",
  "fulltext_url": null,
  "id": "fck3c7wb4522h",
  "issns": [],
  "landing_url": "https://hal.science/hal-05780162v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdu"
    ],
    [
      "hal",
      "0.stat"
    ],
    [
      "hal",
      "1.sdu.stu"
    ],
    [
      "hal",
      "1.stat.ml"
    ],
    [
      "hal",
      "2.sdu.stu.gl"
    ],
    [
      "openalex",
      "1902|Cryospheric studies and observations"
    ]
  ],
  "openalex": "W7215037933",
  "pdf_url": "https://hal.science/hal-05780162/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fck3c7wb4522h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:48Z",
      "native_id": "hal-05780162",
      "source": "hal",
      "url": "https://hal.science/hal-05780162v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:2.sdu.stu.gl",
      "subfield": "oceanography"
    },
    {
      "field": "mathematics",
      "origin": "hal:0.stat",
      "subfield": "statistics"
    }
  ],
  "title": "Multi-sensor deep-learning mapping of surface and basal fractures across Antarctic ice shelves during the last four decades",
  "title_text": "Multi-sensor deep-learning mapping of surface and basal fractures across Antarctic ice shelves during the last four decades",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:48Z",
  "url": "https://research.draconitic.com/paper/fck3c7wb4522h",
  "venue": "HAL",
  "versions": []
}