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: 10,945 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 16 Apr 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 8,523 | 2,226 | 196 | 10,945 | 249 | 268 |
- HTML: 8,523
- PDF: 2,226
- XML: 196
- Total: 10,945
- BibTeX: 249
- EndNote: 268
Total article views: 9,419 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Nov 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 7,790 | 1,483 | 146 | 9,419 | 195 | 221 |
- HTML: 7,790
- PDF: 1,483
- XML: 146
- Total: 9,419
- BibTeX: 195
- EndNote: 221
Total article views: 1,526 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 16 Apr 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 733 | 743 | 50 | 1,526 | 54 | 47 |
- HTML: 733
- PDF: 743
- XML: 50
- Total: 1,526
- BibTeX: 54
- EndNote: 47
Viewed (geographical distribution)
Total article views: 10,945 (including HTML, PDF, and XML)
Thereof 10,063 with geography defined
and 882 with unknown origin.
Total article views: 9,419 (including HTML, PDF, and XML)
Thereof 8,954 with geography defined
and 465 with unknown origin.
Total article views: 1,526 (including HTML, PDF, and XML)
Thereof 1,109 with geography defined
and 417 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
26 citations as recorded by crossref.
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. https://doi.org/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. https://doi.org/10.1016/j.oregeorev.2022.105119
- Antarctic Ice Sheet grounding line discharge from 1996–2024 B. Davison et al. https://doi.org/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. https://doi.org/10.3390/rs15051359
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. https://doi.org/10.1016/j.cageo.2025.105857
- Analysis of high-resolution basal melt rate in the southernmost end of Amery Ice Shelf M. Liu et al. https://doi.org/10.1080/10095020.2026.2730043
- Shipborne bathymetry constrained denoising diffusion probabilistic model for seafloor DEM super-resolution Y. Fan et al. https://doi.org/10.1016/j.jag.2025.104953
- Paths forward in radioglaciology D. Schroeder https://doi.org/10.1017/aog.2023.3
- Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al. https://doi.org/10.1017/jog.2021.122
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. https://doi.org/10.1017/jog.2021.120
- Synergistic Integration of Inpainting and Super-Resolution: A New Pathway Toward High-Precision Subglacial Topography Generation Z. Jiang et al. https://doi.org/10.1109/TGRS.2026.3710481
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. https://doi.org/10.3390/rs15112708
- Synthetic bed topographies for Antarctica and their utility in ice sheet modelling F. McCormack et al. https://doi.org/10.1098/rsta.2024.0537
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. https://doi.org/10.5194/tc-19-645-2025
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. https://doi.org/10.1038/s41597-025-04672-y
- A Glacier Ice Thickness Estimation Method Based on Deep Convolutional Neural Networks Z. Li et al. https://doi.org/10.3390/geosciences15070242
- A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S K. Shahateet et al. https://doi.org/10.5194/tc-19-1577-2025
- Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al. https://doi.org/10.1017/aog.2023.67
- Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al. https://doi.org/10.1126/sciadv.abj8138
- UAVs for Science in Antarctica P. Pina & G. Vieira https://doi.org/10.3390/rs14071610
- Advancing cryospheric studies: a historical perspective on radio-echo soundgram analysis techniques A. Awati et al. https://doi.org/10.1007/s12145-025-01996-6
- An Arctic underwater terrain matching method integrating template matching and DEM super-resolution W. Huang et al. https://doi.org/10.1016/j.jag.2026.105251
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. https://doi.org/10.5194/gmd-16-6671-2023
- Complex mesoscale landscapes beneath Antarctica mapped from space H. Ockenden et al. https://doi.org/10.1126/science.ady2532
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. https://doi.org/10.1109/TGRS.2023.3303231
- Harnessing machine learning to improve ice sheet bed mapping S. Palmer & C. Kirkwood https://doi.org/10.1098/rsta.2024.0539
26 citations as recorded by crossref.
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. https://doi.org/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. https://doi.org/10.1016/j.oregeorev.2022.105119
- Antarctic Ice Sheet grounding line discharge from 1996–2024 B. Davison et al. https://doi.org/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. https://doi.org/10.3390/rs15051359
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. https://doi.org/10.1016/j.cageo.2025.105857
- Analysis of high-resolution basal melt rate in the southernmost end of Amery Ice Shelf M. Liu et al. https://doi.org/10.1080/10095020.2026.2730043
- Shipborne bathymetry constrained denoising diffusion probabilistic model for seafloor DEM super-resolution Y. Fan et al. https://doi.org/10.1016/j.jag.2025.104953
- Paths forward in radioglaciology D. Schroeder https://doi.org/10.1017/aog.2023.3
- Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al. https://doi.org/10.1017/jog.2021.122
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. https://doi.org/10.1017/jog.2021.120
- Synergistic Integration of Inpainting and Super-Resolution: A New Pathway Toward High-Precision Subglacial Topography Generation Z. Jiang et al. https://doi.org/10.1109/TGRS.2026.3710481
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. https://doi.org/10.3390/rs15112708
- Synthetic bed topographies for Antarctica and their utility in ice sheet modelling F. McCormack et al. https://doi.org/10.1098/rsta.2024.0537
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. https://doi.org/10.5194/tc-19-645-2025
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. https://doi.org/10.1038/s41597-025-04672-y
- A Glacier Ice Thickness Estimation Method Based on Deep Convolutional Neural Networks Z. Li et al. https://doi.org/10.3390/geosciences15070242
- A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S K. Shahateet et al. https://doi.org/10.5194/tc-19-1577-2025
- Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al. https://doi.org/10.1017/aog.2023.67
- Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al. https://doi.org/10.1126/sciadv.abj8138
- UAVs for Science in Antarctica P. Pina & G. Vieira https://doi.org/10.3390/rs14071610
- Advancing cryospheric studies: a historical perspective on radio-echo soundgram analysis techniques A. Awati et al. https://doi.org/10.1007/s12145-025-01996-6
- An Arctic underwater terrain matching method integrating template matching and DEM super-resolution W. Huang et al. https://doi.org/10.1016/j.jag.2026.105251
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. https://doi.org/10.5194/gmd-16-6671-2023
- Complex mesoscale landscapes beneath Antarctica mapped from space H. Ockenden et al. https://doi.org/10.1126/science.ady2532
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. https://doi.org/10.1109/TGRS.2023.3303231
- Harnessing machine learning to improve ice sheet bed mapping S. Palmer & C. Kirkwood https://doi.org/10.1098/rsta.2024.0539
Saved (final revised paper)
Latest update: 28 Sep 2026
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...
