DeepBedMap: a deep neural network for resolving the bed topography of Antarctica
<p>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...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-11-01
|
Series: | The Cryosphere |
Online Access: | https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf |
_version_ | 1819086087935492096 |
---|---|
author | W. J. Leong H. J. Horgan |
author_facet | W. J. Leong H. J. Horgan |
author_sort | W. J. Leong |
collection | DOAJ |
description | <p>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 <span class="inline-formula">m</span>) 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 <span class="inline-formula">m</span>) 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 <span class="inline-formula">m</span>) 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.</p> |
first_indexed | 2024-12-21T21:14:41Z |
format | Article |
id | doaj.art-3c64fbbe2f9a4386942da242a12c5b37 |
institution | Directory Open Access Journal |
issn | 1994-0416 1994-0424 |
language | English |
last_indexed | 2024-12-21T21:14:41Z |
publishDate | 2020-11-01 |
publisher | Copernicus Publications |
record_format | Article |
series | The Cryosphere |
spelling | doaj.art-3c64fbbe2f9a4386942da242a12c5b372022-12-21T18:50:03ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242020-11-01143687370510.5194/tc-14-3687-2020DeepBedMap: a deep neural network for resolving the bed topography of AntarcticaW. J. LeongH. J. Horgan<p>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 <span class="inline-formula">m</span>) 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 <span class="inline-formula">m</span>) 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 <span class="inline-formula">m</span>) 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.</p>https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf |
spellingShingle | W. J. Leong H. J. Horgan DeepBedMap: a deep neural network for resolving the bed topography of Antarctica The Cryosphere |
title | 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_short | 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 |
url | https://tc.copernicus.org/articles/14/3687/2020/tc-14-3687-2020.pdf |
work_keys_str_mv | AT wjleong deepbedmapadeepneuralnetworkforresolvingthebedtopographyofantarctica AT hjhorgan deepbedmapadeepneuralnetworkforresolvingthebedtopographyofantarctica |