Articles | Volume 14, issue 11
https://doi.org/10.5194/tc-14-3687-2020
© Author(s) 2020. 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-14-3687-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Nov 2020
Research article |
| 05 Nov 2020
| 05 Nov 2020
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica
Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
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Total article views: 6,000 (including HTML, PDF, and XML)
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Total article views: 7,353 (including HTML, PDF, and XML)
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Total article views: 6,000 (including HTML, PDF, and XML)
Thereof 5,710 with geography defined
and 290 with unknown origin.
Total article views: 1,353 (including HTML, PDF, and XML)
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Cited
18 citations as recorded by crossref.
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. 10.1038/s41597-025-04672-y
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane L. Smith et al. 10.1016/j.oregeorev.2022.105119
- A Glacier Ice Thickness Estimation Method Based on Deep Convolutional Neural Networks Z. Li et al. 10.3390/geosciences15070242
- Antarctic Ice Sheet grounding line discharge from 1996–2024 B. Davison et al. 10.5194/essd-17-3259-2025
- Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al. 10.3390/rs15051359
- A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S K. Shahateet et al. 10.5194/tc-19-1577-2025
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. 10.1016/j.cageo.2025.105857
- Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al. 10.1017/aog.2023.67
- Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al. 10.1126/sciadv.abj8138
- UAVs for Science in Antarctica P. Pina & G. Vieira 10.3390/rs14071610
- Paths forward in radioglaciology D. Schroeder 10.1017/aog.2023.3
- Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al. 10.1017/jog.2021.122
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. 10.3390/rs15112708
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. 10.1109/TGRS.2023.3303231
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. 10.5194/tc-19-645-2025
18 citations as recorded by crossref.
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. 10.1038/s41597-025-04672-y
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane L. Smith et al. 10.1016/j.oregeorev.2022.105119
- A Glacier Ice Thickness Estimation Method Based on Deep Convolutional Neural Networks Z. Li et al. 10.3390/geosciences15070242
- Antarctic Ice Sheet grounding line discharge from 1996–2024 B. Davison et al. 10.5194/essd-17-3259-2025
- Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al. 10.3390/rs15051359
- A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S K. Shahateet et al. 10.5194/tc-19-1577-2025
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. 10.1016/j.cageo.2025.105857
- Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al. 10.1017/aog.2023.67
- Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al. 10.1126/sciadv.abj8138
- UAVs for Science in Antarctica P. Pina & G. Vieira 10.3390/rs14071610
- Paths forward in radioglaciology D. Schroeder 10.1017/aog.2023.3
- Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al. 10.1017/jog.2021.122
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. 10.3390/rs15112708
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. 10.1109/TGRS.2023.3303231
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. 10.5194/tc-19-645-2025
Latest update: 08 Aug 2025
Short summary
A machine learning technique similar to the one used to enhance everyday photographs is applied to the problem of getting a better picture of Antarctica's bed – the part which is hidden beneath the ice. By taking hints from what satellites can observe at the ice surface, the novel method learns to generate a rougher bed topography that complements existing approaches, with a result that is able to be used by scientists running fine-scale ice sheet models relevant to predicting future sea levels.
A machine learning technique similar to the one used to enhance everyday photographs is applied...
