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
Viewed
Total article views: 7,241 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 16 Apr 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 5,576 | 1,550 | 115 | 7,241 | 152 | 121 |
- HTML: 5,576
- PDF: 1,550
- XML: 115
- Total: 7,241
- BibTeX: 152
- EndNote: 121
Total article views: 5,893 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Nov 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,876 | 940 | 77 | 5,893 | 106 | 84 |
- HTML: 4,876
- PDF: 940
- XML: 77
- Total: 5,893
- BibTeX: 106
- EndNote: 84
Total article views: 1,348 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 16 Apr 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 700 | 610 | 38 | 1,348 | 46 | 37 |
- HTML: 700
- PDF: 610
- XML: 38
- Total: 1,348
- BibTeX: 46
- EndNote: 37
Viewed (geographical distribution)
Total article views: 7,241 (including HTML, PDF, and XML)
Thereof 6,539 with geography defined
and 702 with unknown origin.
Total article views: 5,893 (including HTML, PDF, and XML)
Thereof 5,603 with geography defined
and 290 with unknown origin.
Total article views: 1,348 (including HTML, PDF, and XML)
Thereof 936 with geography defined
and 412 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
17 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
- 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
17 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
- 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: 03 Jul 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...
