Multi-sensor deep-learning mapping of surface and basal fractures across Antarctic ice shelves during the last four decades
Year: 2026
Type: preprint
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.
Institutions: Instituto Tecnológico de Aeronáutica, Institut des Géosciences de l'Environnement, Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Institut de Recherche pour le Développement, Université Grenoble Alpes, University of Copenhagen, Vrije Universiteit Brussel, University of California, Irvine
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7215037933