DeepBedMap: A super-resolution neural network created bed topography of Antarctica
Going beyond BEDMAP2 using a super resolution deep neural network. deepbedmap_v1.1.0.zip: Python code for the DeepBedMap Super-Resolution Generative Adversarial Network. deepbedmap_dem.tif: Digital Elevation Model (250 m spatial resolution) in GeoTiff format, using Antarctic Polar Stereographic Proj...
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ftdatacite:10.5281/zenodo.3752613 2023-05-15T13:53:29+02:00 DeepBedMap: A super-resolution neural network created bed topography of Antarctica Leong, Wei Ji Horgan, Huw Joseph 2020 https://dx.doi.org/10.5281/zenodo.3752613 https://zenodo.org/record/3752613 en eng Zenodo https://dx.doi.org/10.5194/tc-14-3687-2020 https://dx.doi.org/10.5281/zenodo.3752614 https://dx.doi.org/10.5281/zenodo.4054246 Open Access GNU Lesser General Public License v3.0 or later https://www.gnu.org/licenses/lgpl-3.0-standalone.html lgpl-3.0+ info:eu-repo/semantics/openAccess Antarctica Digital Elevation Model Super-resolution Neural Network dataset Dataset 2020 ftdatacite https://doi.org/10.5281/zenodo.3752613 https://doi.org/10.5194/tc-14-3687-2020 https://doi.org/10.5281/zenodo.3752614 https://doi.org/10.5281/zenodo.4054246 2021-11-05T12:55:41Z Going beyond BEDMAP2 using a super resolution deep neural network. deepbedmap_v1.1.0.zip: Python code for the DeepBedMap Super-Resolution Generative Adversarial Network. deepbedmap_dem.tif: Digital Elevation Model (250 m spatial resolution) in GeoTiff format, using Antarctic Polar Stereographic Projection (EPSG:3031). srgan_generator_model_weights.npz: The Generator neural network weights/parameters as a NumPy zip file. Dataset Antarc* Antarctic Antarctica DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Antarctica Digital Elevation Model Super-resolution Neural Network |
spellingShingle |
Antarctica Digital Elevation Model Super-resolution Neural Network Leong, Wei Ji Horgan, Huw Joseph DeepBedMap: A super-resolution neural network created bed topography of Antarctica |
topic_facet |
Antarctica Digital Elevation Model Super-resolution Neural Network |
description |
Going beyond BEDMAP2 using a super resolution deep neural network. deepbedmap_v1.1.0.zip: Python code for the DeepBedMap Super-Resolution Generative Adversarial Network. deepbedmap_dem.tif: Digital Elevation Model (250 m spatial resolution) in GeoTiff format, using Antarctic Polar Stereographic Projection (EPSG:3031). srgan_generator_model_weights.npz: The Generator neural network weights/parameters as a NumPy zip file. |
format |
Dataset |
author |
Leong, Wei Ji Horgan, Huw Joseph |
author_facet |
Leong, Wei Ji Horgan, Huw Joseph |
author_sort |
Leong, Wei Ji |
title |
DeepBedMap: A super-resolution neural network created bed topography of Antarctica |
title_short |
DeepBedMap: A super-resolution neural network created bed topography of Antarctica |
title_full |
DeepBedMap: A super-resolution neural network created bed topography of Antarctica |
title_fullStr |
DeepBedMap: A super-resolution neural network created bed topography of Antarctica |
title_full_unstemmed |
DeepBedMap: A super-resolution neural network created bed topography of Antarctica |
title_sort |
deepbedmap: a super-resolution neural network created bed topography of antarctica |
publisher |
Zenodo |
publishDate |
2020 |
url |
https://dx.doi.org/10.5281/zenodo.3752613 https://zenodo.org/record/3752613 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Antarctica |
genre_facet |
Antarc* Antarctic Antarctica |
op_relation |
https://dx.doi.org/10.5194/tc-14-3687-2020 https://dx.doi.org/10.5281/zenodo.3752614 https://dx.doi.org/10.5281/zenodo.4054246 |
op_rights |
Open Access GNU Lesser General Public License v3.0 or later https://www.gnu.org/licenses/lgpl-3.0-standalone.html lgpl-3.0+ info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5281/zenodo.3752613 https://doi.org/10.5194/tc-14-3687-2020 https://doi.org/10.5281/zenodo.3752614 https://doi.org/10.5281/zenodo.4054246 |
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