CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models
This repository contains data and code used in the paper "Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models" by Abolt et al . in The Cryosphere (2019). See the included README file for instructions on usage.
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ftzenodo:oai:zenodo.org:2537167 2024-09-15T18:11:26+00:00 CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models Abolt, Charles J Young, Michael H Atchley, Adam L Wilson, Cathy J 2019-01-25 https://doi.org/10.5821/zenodo.2537167 eng eng Zenodo https://doi.org/10.5194/tc-13-237-2019 https://doi.org/10.5821/zenodo.2537167 oai:zenodo.org:2537167 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode ice wedge polygon permafrost thermokarst deep learning machine learning convolutional neural network info:eu-repo/semantics/other 2019 ftzenodo https://doi.org/10.5821/zenodo.253716710.5194/tc-13-237-2019 2024-07-26T09:55:03Z This repository contains data and code used in the paper "Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models" by Abolt et al . in The Cryosphere (2019). See the included README file for instructions on usage. Other/Unknown Material Ice permafrost Thermokarst wedge* Zenodo |
institution |
Open Polar |
collection |
Zenodo |
op_collection_id |
ftzenodo |
language |
English |
topic |
ice wedge polygon permafrost thermokarst deep learning machine learning convolutional neural network |
spellingShingle |
ice wedge polygon permafrost thermokarst deep learning machine learning convolutional neural network Abolt, Charles J Young, Michael H Atchley, Adam L Wilson, Cathy J CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
topic_facet |
ice wedge polygon permafrost thermokarst deep learning machine learning convolutional neural network |
description |
This repository contains data and code used in the paper "Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models" by Abolt et al . in The Cryosphere (2019). See the included README file for instructions on usage. |
format |
Other/Unknown Material |
author |
Abolt, Charles J Young, Michael H Atchley, Adam L Wilson, Cathy J |
author_facet |
Abolt, Charles J Young, Michael H Atchley, Adam L Wilson, Cathy J |
author_sort |
Abolt, Charles J |
title |
CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
title_short |
CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
title_full |
CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
title_fullStr |
CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
title_full_unstemmed |
CNN-watershed: A machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
title_sort |
cnn-watershed: a machine learning-based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://doi.org/10.5821/zenodo.2537167 |
genre |
Ice permafrost Thermokarst wedge* |
genre_facet |
Ice permafrost Thermokarst wedge* |
op_relation |
https://doi.org/10.5194/tc-13-237-2019 https://doi.org/10.5821/zenodo.2537167 oai:zenodo.org:2537167 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5821/zenodo.253716710.5194/tc-13-237-2019 |
_version_ |
1810449025870069760 |