Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms
Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimetre resolution against the snow depth. The resulting depth-force profile can be parameterized...
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00063813 2023-05-15T15:09:44+02:00 Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms Kaltenborn, Julia Macfarlane, Amy R. Clay, Viviane Schneebeli, Martin 2022-12 electronic https://doi.org/10.5194/egusphere-2022-938 https://noa.gwlb.de/receive/cop_mods_00063813 https://egusphere.copernicus.org/preprints/egusphere-2022-938/egusphere-2022-938.pdf eng eng Copernicus Publications https://doi.org/10.5194/egusphere-2022-938 https://noa.gwlb.de/receive/cop_mods_00063813 https://egusphere.copernicus.org/preprints/egusphere-2022-938/egusphere-2022-938.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/restrictedAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/egusphere-2022-938 2022-12-12T00:12:47Z Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimetre resolution against the snow depth. The resulting depth-force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is in theory possible, but can either not be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. To address this gap, we evaluate how well machine learning models can automatically segment and classify SMP profiles. We trained fourteen different models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, a large collection of snow profiles on Arctic sea ice. We found that SMP profiles can be successfully segmented and classified into snow classes, based solely on the SMP's signal. The model comparison provided in this study enables practitioners to choose a model that is suitable for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Overall, snowdragon creates a link between traditional snow classification and high-resolution force-depth profiles. With such a tool, traditional snow profile observations can be compared to SMP profiles. Article in Journal/Newspaper Arctic Sea ice Niedersächsisches Online-Archiv NOA Arctic |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Kaltenborn, Julia Macfarlane, Amy R. Clay, Viviane Schneebeli, Martin Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms |
topic_facet |
article Verlagsveröffentlichung |
description |
Snow-layer segmentation and classification is an essential diagnostic task for a wide variety of cryospheric applications. The SnowMicroPen (SMP) measures the snowpack's penetration force at submillimetre resolution against the snow depth. The resulting depth-force profile can be parameterized for density and specific surface area. However, no information on traditional snow types is currently extracted automatically. The labeling of snow types is a time-intensive task that requires practice and becomes infeasible for large datasets. Previous work showed that automated segmentation and classification is in theory possible, but can either not be applied to data straight from the field or needs additional time-costly information, such as from classified snow pits. To address this gap, we evaluate how well machine learning models can automatically segment and classify SMP profiles. We trained fourteen different models, among them semi-supervised models and artificial neural networks (ANNs), on the MOSAiC SMP dataset, a large collection of snow profiles on Arctic sea ice. We found that SMP profiles can be successfully segmented and classified into snow classes, based solely on the SMP's signal. The model comparison provided in this study enables practitioners to choose a model that is suitable for their task and dataset. The findings presented will facilitate and accelerate snow type identification through SMP profiles. Overall, snowdragon creates a link between traditional snow classification and high-resolution force-depth profiles. With such a tool, traditional snow profile observations can be compared to SMP profiles. |
format |
Article in Journal/Newspaper |
author |
Kaltenborn, Julia Macfarlane, Amy R. Clay, Viviane Schneebeli, Martin |
author_facet |
Kaltenborn, Julia Macfarlane, Amy R. Clay, Viviane Schneebeli, Martin |
author_sort |
Kaltenborn, Julia |
title |
Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms |
title_short |
Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms |
title_full |
Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms |
title_fullStr |
Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms |
title_full_unstemmed |
Automatic classification and segmentation of Snow Micro Penetrometer profiles with machine learning algorithms |
title_sort |
automatic classification and segmentation of snow micro penetrometer profiles with machine learning algorithms |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/egusphere-2022-938 https://noa.gwlb.de/receive/cop_mods_00063813 https://egusphere.copernicus.org/preprints/egusphere-2022-938/egusphere-2022-938.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
https://doi.org/10.5194/egusphere-2022-938 https://noa.gwlb.de/receive/cop_mods_00063813 https://egusphere.copernicus.org/preprints/egusphere-2022-938/egusphere-2022-938.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/restrictedAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/egusphere-2022-938 |
_version_ |
1766340863697354752 |