Multi-sensor deep-learning mapping of surface and basal fractures across Antarctic ice shelves during the last four decades
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.
Related
- Impact of wind variations on surface variability over the Patagonian Continental Shelves
- Dry snow initialization and densification over the Greenland and Antarctic ice sheets in the ORCHIDEE land surface model
- Sensitivity of the Bootstrap sea ice concentration algorithm to surface parameters in the Antarctic marginal ice zone using passive microwave retrievals
- The role of brittle shear zones and the associated fractures in the formation and characteristics of bedrock surface depressions in crystalline bedrock (Turku, southwestern Finland)
- Improved recovery of sub-ice shelf bathymetry from gravity data using an isostatic correction: a case study from the Dotson and Crosson ice shelves, West Antarctica
BibTeX
@misc{shahateet2026multisensor,
title = {{Multi-sensor deep-learning mapping of surface and basal fractures across Antarctic ice shelves during the last four decades}},
author = {Kaian Shahateet and Colin Prieur and Romain Millan and Lucille Gimenes and Andrea Sofie Haastrup-Vang and Jaime Roas and Maaike Izeboud and Raphaelle Charrassin and Emil Giraldi and Romain Santiard},
howpublished = {HAL},
year = {2026},
month = oct,
doi = {10.5194/egusphere-2026-5765},
url = {https://hal.science/hal-05780162v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multi-sensor deep-learning mapping of surface and basal fractures across Antarctic ice shelves during the last four decades
AU - Kaian Shahateet
AU - Colin Prieur
AU - Romain Millan
AU - Lucille Gimenes
AU - Andrea Sofie Haastrup-Vang
AU - Jaime Roas
AU - Maaike Izeboud
AU - Raphaelle Charrassin
AU - Emil Giraldi
AU - Romain Santiard
PY - 2026
DA - 2026/10/01
PB - HAL
DO - 10.5194/egusphere-2026-5765
UR - https://hal.science/hal-05780162v1
AB - 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.
ER -