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: 9,981 (including HTML, PDF, and XML)
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Total article views: 8,529 (including HTML, PDF, and XML)
Thereof 8,169 with geography defined
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Total article views: 1,452 (including HTML, PDF, and XML)
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Cited
24 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.
- Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane L. Smith et al.
- Antarctic Ice Sheet grounding line discharge from 1996–2024 B. Davison et al.
- Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al.
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al.
- Shipborne bathymetry constrained denoising diffusion probabilistic model for seafloor DEM super-resolution Y. Fan et al.
- Paths forward in radioglaciology D. Schroeder
- Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al.
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al.
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al.
- Synthetic bed topographies for Antarctica and their utility in ice sheet modelling F. McCormack et al.
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al.
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al.
- A Glacier Ice Thickness Estimation Method Based on Deep Convolutional Neural Networks Z. Li et al.
- A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S K. Shahateet et al.
- Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al.
- Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al.
- UAVs for Science in Antarctica P. Pina & G. Vieira
- Advancing cryospheric studies: a historical perspective on radio-echo soundgram analysis techniques A. Awati et al.
- An Arctic underwater terrain matching method integrating template matching and DEM super-resolution W. Huang et al.
- Universal differential equations for glacier ice flow modelling J. Bolibar et al.
- Complex mesoscale landscapes beneath Antarctica mapped from space H. Ockenden et al.
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al.
- Harnessing machine learning to improve ice sheet bed mapping S. Palmer & C. Kirkwood
24 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.
- Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane L. Smith et al.
- Antarctic Ice Sheet grounding line discharge from 1996–2024 B. Davison et al.
- Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data Y. Cai et al.
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al.
- Shipborne bathymetry constrained denoising diffusion probabilistic model for seafloor DEM super-resolution Y. Fan et al.
- Paths forward in radioglaciology D. Schroeder
- Using bed-roughness signatures to characterise glacial landform assemblages beneath palaeo-ice sheets F. Falcini et al.
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al.
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al.
- Synthetic bed topographies for Antarctica and their utility in ice sheet modelling F. McCormack et al.
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al.
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al.
- A Glacier Ice Thickness Estimation Method Based on Deep Convolutional Neural Networks Z. Li et al.
- A reconstruction of the ice thickness of the Antarctic Peninsula Ice Sheet north of 70° S K. Shahateet et al.
- Estimating ice discharge of the Antarctic Peninsula using different ice-thickness datasets K. Shahateet et al.
- Unexplored Antarctic meteorite collection sites revealed through machine learning V. Tollenaar et al.
- UAVs for Science in Antarctica P. Pina & G. Vieira
- Advancing cryospheric studies: a historical perspective on radio-echo soundgram analysis techniques A. Awati et al.
- An Arctic underwater terrain matching method integrating template matching and DEM super-resolution W. Huang et al.
- Universal differential equations for glacier ice flow modelling J. Bolibar et al.
- Complex mesoscale landscapes beneath Antarctica mapped from space H. Ockenden et al.
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al.
- Harnessing machine learning to improve ice sheet bed mapping S. Palmer & C. Kirkwood
Saved (final revised paper)
Latest update: 29 Apr 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...
