Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models
We present a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network used to detect pixels representing polygon boundaries. A watershed transformation...
Published in: | The Cryosphere |
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Format: | Article in Journal/Newspaper |
Language: | English |
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Copernicus Publications
2019
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Online Access: | https://doi.org/10.5194/tc-13-237-2019 https://www.the-cryosphere.net/13/237/2019/tc-13-237-2019.pdf https://doaj.org/article/b989ebc5f8114d9fb04ee30e276e6b59 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:b989ebc5f8114d9fb04ee30e276e6b59 2023-05-15T15:39:41+02:00 Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models C. J. Abolt M. H. Young A. L. Atchley C. J. Wilson 2019-01-01 https://doi.org/10.5194/tc-13-237-2019 https://www.the-cryosphere.net/13/237/2019/tc-13-237-2019.pdf https://doaj.org/article/b989ebc5f8114d9fb04ee30e276e6b59 en eng Copernicus Publications doi:10.5194/tc-13-237-2019 1994-0416 1994-0424 https://www.the-cryosphere.net/13/237/2019/tc-13-237-2019.pdf https://doaj.org/article/b989ebc5f8114d9fb04ee30e276e6b59 undefined The Cryosphere, Vol 13, Pp 237-245 (2019) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2019 fttriple https://doi.org/10.5194/tc-13-237-2019 2023-01-22T19:12:19Z We present a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network used to detect pixels representing polygon boundaries. A watershed transformation is subsequently used to segment imagery into discrete polygons. Fast training times (<5 min) permit an iterative approach to improving skill as the routine is applied across broad landscapes. Results from study sites near Utqiaġvik (formerly Barrow) and Prudhoe Bay, Alaska, demonstrate robust performance in diverse tundra settings, with manual validations demonstrating 70–96 % accuracy by area at the kilometer scale. The methodology permits precise, spatially extensive measurements of polygonal microtopography and trough network geometry. Article in Journal/Newspaper Barrow Prudhoe Bay The Cryosphere Tundra Alaska Unknown The Cryosphere 13 1 237 245 |
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language |
English |
topic |
geo envir |
spellingShingle |
geo envir C. J. Abolt M. H. Young A. L. Atchley C. J. Wilson Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
topic_facet |
geo envir |
description |
We present a workflow for the rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. At the core of the workflow is a convolutional neural network used to detect pixels representing polygon boundaries. A watershed transformation is subsequently used to segment imagery into discrete polygons. Fast training times (<5 min) permit an iterative approach to improving skill as the routine is applied across broad landscapes. Results from study sites near Utqiaġvik (formerly Barrow) and Prudhoe Bay, Alaska, demonstrate robust performance in diverse tundra settings, with manual validations demonstrating 70–96 % accuracy by area at the kilometer scale. The methodology permits precise, spatially extensive measurements of polygonal microtopography and trough network geometry. |
format |
Article in Journal/Newspaper |
author |
C. J. Abolt M. H. Young A. L. Atchley C. J. Wilson |
author_facet |
C. J. Abolt M. H. Young A. L. Atchley C. J. Wilson |
author_sort |
C. J. Abolt |
title |
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
title_short |
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
title_full |
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
title_fullStr |
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
title_full_unstemmed |
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
title_sort |
brief communication: rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-13-237-2019 https://www.the-cryosphere.net/13/237/2019/tc-13-237-2019.pdf https://doaj.org/article/b989ebc5f8114d9fb04ee30e276e6b59 |
genre |
Barrow Prudhoe Bay The Cryosphere Tundra Alaska |
genre_facet |
Barrow Prudhoe Bay The Cryosphere Tundra Alaska |
op_source |
The Cryosphere, Vol 13, Pp 237-245 (2019) |
op_relation |
doi:10.5194/tc-13-237-2019 1994-0416 1994-0424 https://www.the-cryosphere.net/13/237/2019/tc-13-237-2019.pdf https://doaj.org/article/b989ebc5f8114d9fb04ee30e276e6b59 |
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undefined |
op_doi |
https://doi.org/10.5194/tc-13-237-2019 |
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The Cryosphere |
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13 |
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1 |
container_start_page |
237 |
op_container_end_page |
245 |
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