Articles | Volume 17, issue 10
https://doi.org/10.5194/tc-17-4421-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-4421-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
19 Oct 2023
Research article | Highlight paper |
| 19 Oct 2023
| 19 Oct 2023
Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery
School of Earth and Environment, University of Leeds, Leeds, United Kingdom
Anna E. Hogg
School of Earth and Environment, University of Leeds, Leeds, United Kingdom
Stephen L. Cornford
School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
David C. Hogg
School of Computing, University of Leeds, Leeds, United Kingdom
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Cited
17 citations as recorded by crossref.
- Inland migration of near-surface crevasses in the Amundsen Sea Sector, West Antarctica A. Hoffman et al. https://doi.org/10.5194/tc-19-1353-2025
- The dynamics of Trooz Glacier, Antarctic Peninsula, by satellite remote sensing data S. Kadurin & V. Kadurin https://doi.org/10.33275/1727-7485.2.2023.713
- A robust multitask deep learning algorithm for Antarctic ice shelf fracture detection from multisource satellite imagery Z. Huang et al. https://doi.org/10.1016/j.rse.2025.114964
- Brief communication: Tides and damage as drivers of lake drainages on Shackleton Ice Shelf J. Sommer et al. https://doi.org/10.5194/tc-19-5903-2025
- Damage development on Antarctic ice shelves sensitive to climate warming M. Izeboud et al. https://doi.org/10.1038/s41558-025-02453-4
- Three-Dimensional Characterization of Pan-Antarctic Ice Shelf Fracture: An Integrated Deep Learning and Hydrological Analysis Framework Q. Li et al. https://doi.org/10.1109/LGRS.2025.3595934
- Using observations of surface fracture to address ill-posed ice softness estimation over Pine Island Glacier T. Surawy-Stepney et al. https://doi.org/10.5194/tc-19-5531-2025
- Spatio-temporal melt and basal channel evolution on Pine Island Glacier ice shelf from CryoSat-2 K. Lowery et al. https://doi.org/10.5194/tc-19-4893-2025
- Automated crevasse mapping for Alpine glaciers: A multitask deep neural network approach C. Baumhoer et al. https://doi.org/10.1016/j.jag.2025.104495
- A Framework for Characterizing 3-D Structures of Crevasses and Rifts Across Antarctic Ice Shelves A. Pang et al. https://doi.org/10.1109/TGRS.2025.3645189
- ICI-YOLOv8 Rapid Identification of Antarctic Sea Ice Cracks and Numerical Analysis of Monte Carlo Simulation Under Probability Distribution X. Chang et al. https://doi.org/10.3390/rs17213646
- CREVNet: A Transformer and CNN-Based Network for Accurate Segmentation of Ice Shelf Crevasses K. Zheng et al. https://doi.org/10.1109/LGRS.2024.3407860
- Increased crevassing across accelerating Greenland Ice Sheet margins T. Chudley et al. https://doi.org/10.1038/s41561-024-01636-6
- Glacier damage evolution over ice flow timescales M. Ranganathan et al. https://doi.org/10.5194/tc-19-1599-2025
- Weak relationship between remotely detected crevasses and inferred ice rheological parameters on Antarctic ice shelves C. Gerli et al. https://doi.org/10.5194/tc-18-2677-2024
- Speed-up, slowdown, and redirection of ice flow on neighbouring ice streams in the Pope, Smith, and Kohler region of West Antarctica H. Selley et al. https://doi.org/10.5194/tc-19-1725-2025
- Extracting Antarctic ice shelf fracture depths using the linear cloth simulation filtering algorithm B. Xu et al. https://doi.org/10.1016/j.jag.2026.105255
17 citations as recorded by crossref.
- Inland migration of near-surface crevasses in the Amundsen Sea Sector, West Antarctica A. Hoffman et al. https://doi.org/10.5194/tc-19-1353-2025
- The dynamics of Trooz Glacier, Antarctic Peninsula, by satellite remote sensing data S. Kadurin & V. Kadurin https://doi.org/10.33275/1727-7485.2.2023.713
- A robust multitask deep learning algorithm for Antarctic ice shelf fracture detection from multisource satellite imagery Z. Huang et al. https://doi.org/10.1016/j.rse.2025.114964
- Brief communication: Tides and damage as drivers of lake drainages on Shackleton Ice Shelf J. Sommer et al. https://doi.org/10.5194/tc-19-5903-2025
- Damage development on Antarctic ice shelves sensitive to climate warming M. Izeboud et al. https://doi.org/10.1038/s41558-025-02453-4
- Three-Dimensional Characterization of Pan-Antarctic Ice Shelf Fracture: An Integrated Deep Learning and Hydrological Analysis Framework Q. Li et al. https://doi.org/10.1109/LGRS.2025.3595934
- Using observations of surface fracture to address ill-posed ice softness estimation over Pine Island Glacier T. Surawy-Stepney et al. https://doi.org/10.5194/tc-19-5531-2025
- Spatio-temporal melt and basal channel evolution on Pine Island Glacier ice shelf from CryoSat-2 K. Lowery et al. https://doi.org/10.5194/tc-19-4893-2025
- Automated crevasse mapping for Alpine glaciers: A multitask deep neural network approach C. Baumhoer et al. https://doi.org/10.1016/j.jag.2025.104495
- A Framework for Characterizing 3-D Structures of Crevasses and Rifts Across Antarctic Ice Shelves A. Pang et al. https://doi.org/10.1109/TGRS.2025.3645189
- ICI-YOLOv8 Rapid Identification of Antarctic Sea Ice Cracks and Numerical Analysis of Monte Carlo Simulation Under Probability Distribution X. Chang et al. https://doi.org/10.3390/rs17213646
- CREVNet: A Transformer and CNN-Based Network for Accurate Segmentation of Ice Shelf Crevasses K. Zheng et al. https://doi.org/10.1109/LGRS.2024.3407860
- Increased crevassing across accelerating Greenland Ice Sheet margins T. Chudley et al. https://doi.org/10.1038/s41561-024-01636-6
- Glacier damage evolution over ice flow timescales M. Ranganathan et al. https://doi.org/10.5194/tc-19-1599-2025
- Weak relationship between remotely detected crevasses and inferred ice rheological parameters on Antarctic ice shelves C. Gerli et al. https://doi.org/10.5194/tc-18-2677-2024
- Speed-up, slowdown, and redirection of ice flow on neighbouring ice streams in the Pope, Smith, and Kohler region of West Antarctica H. Selley et al. https://doi.org/10.5194/tc-19-1725-2025
- Extracting Antarctic ice shelf fracture depths using the linear cloth simulation filtering algorithm B. Xu et al. https://doi.org/10.1016/j.jag.2026.105255
Saved (final revised paper)
Latest update: 28 May 2026
Editorial statement
This research is part of an exciting advancement in the field of glaciology, driven by machine learning. The study focuses on crevasse detection, a highly relevant topic from a scientific and logistic perspective. Crevasses may aid surface meltwater to penetrate through the ice thus impacting ice dynamics. Crevasses also pose a logistical challenge for fieldwork in the polar regions.
In this study, the authors are able to automatically spot grounded crevasses using a Convolutional Neural Networks algorithm. One of the focus areas is the Thwaites Glacier, an area that has recently been subject to extensive scientific research due to its importance for the stability of the West Antarctic Ice Sheet.
This research is part of an exciting advancement in the field of glaciology, driven by machine...
Short summary
The presence of crevasses in Antarctica influences how the ice sheet behaves. It is important, therefore, to collect data on the spatial distribution of crevasses and how they are changing. We present a method of mapping crevasses from satellite radar imagery and apply it to 7.5 years of images, covering Antarctica's floating and grounded ice. We develop a method of measuring change in the density of crevasses and quantify increased fracturing in important parts of the West Antarctic Ice Sheet.
The presence of crevasses in Antarctica influences how the ice sheet behaves. It is important,...
