DeepBedMap: a deep neural network for resolving the bed topography of Antarctica
To resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that can produce Antarctic bed topography with adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on scattered...
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2020
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fttriple:oai:gotriple.eu:oai:doaj.org/article:3c64fbbe2f9a4386942da242a12c5b37 2023-05-15T13:50:42+02:00 DeepBedMap: a deep neural network for resolving the bed topography of Antarctica W. J. Leong H. J. Horgan 2020-11-01 https://doi.org/10.5194/tc-14-3687-2020 https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf https://doaj.org/article/3c64fbbe2f9a4386942da242a12c5b37 en eng Copernicus Publications doi:10.5194/tc-14-3687-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf https://doaj.org/article/3c64fbbe2f9a4386942da242a12c5b37 undefined The Cryosphere, Vol 14, Pp 3687-3705 (2020) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.5194/tc-14-3687-2020 2023-01-22T19:15:01Z To resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that can produce Antarctic bed topography with adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high-resolution (250 m) ground-truth bed elevation grids are available. This model is then used to generate high-resolution bed topography in less surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low-spatial-resolution (1000 m) BEDMAP2 raster image as its prior image. It takes in additional high-spatial-resolution datasets, such as ice surface elevation, velocity and snow accumulation, to better inform the bed topography even in the absence of ice thickness data from direct ice-penetrating-radar surveys. The DeepBedMap model is based on an adapted architecture of the Enhanced Super-Resolution Generative Adversarial Network, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four-times-upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain and by ice sheet modellers wanting to run catchment- or continent-scale ice sheet model simulations. We show that DeepBedMap offers a rougher topographic profile compared to the standard bicubically interpolated BEDMAP2 and BedMachine Antarctica and envision it being used where a high-resolution bed elevation model is required. Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Sheet The Cryosphere Unknown Antarctic The Cryosphere 14 11 3687 3705 |
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geo envir W. J. Leong H. J. Horgan DeepBedMap: a deep neural network for resolving the bed topography of Antarctica |
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geo envir |
description |
To resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that can produce Antarctic bed topography with adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high-resolution (250 m) ground-truth bed elevation grids are available. This model is then used to generate high-resolution bed topography in less surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low-spatial-resolution (1000 m) BEDMAP2 raster image as its prior image. It takes in additional high-spatial-resolution datasets, such as ice surface elevation, velocity and snow accumulation, to better inform the bed topography even in the absence of ice thickness data from direct ice-penetrating-radar surveys. The DeepBedMap model is based on an adapted architecture of the Enhanced Super-Resolution Generative Adversarial Network, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four-times-upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain and by ice sheet modellers wanting to run catchment- or continent-scale ice sheet model simulations. We show that DeepBedMap offers a rougher topographic profile compared to the standard bicubically interpolated BEDMAP2 and BedMachine Antarctica and envision it being used where a high-resolution bed elevation model is required. |
format |
Article in Journal/Newspaper |
author |
W. J. Leong H. J. Horgan |
author_facet |
W. J. Leong H. J. Horgan |
author_sort |
W. J. Leong |
title |
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica |
title_short |
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica |
title_full |
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica |
title_fullStr |
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica |
title_full_unstemmed |
DeepBedMap: a deep neural network for resolving the bed topography of Antarctica |
title_sort |
deepbedmap: a deep neural network for resolving the bed topography of antarctica |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-3687-2020 https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf https://doaj.org/article/3c64fbbe2f9a4386942da242a12c5b37 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Antarctica Ice Sheet The Cryosphere |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet The Cryosphere |
op_source |
The Cryosphere, Vol 14, Pp 3687-3705 (2020) |
op_relation |
doi:10.5194/tc-14-3687-2020 1994-0416 1994-0424 https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf https://doaj.org/article/3c64fbbe2f9a4386942da242a12c5b37 |
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op_doi |
https://doi.org/10.5194/tc-14-3687-2020 |
container_title |
The Cryosphere |
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14 |
container_issue |
11 |
container_start_page |
3687 |
op_container_end_page |
3705 |
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