ArcCI Development ...
Sea ice is important to study as it reflects climate change and the state of the environment. ArcCI is a utility which allows users to upload images of sea ice from online databases which can be cropped into smaller resolutions, which are segmented using a variety of algorithms via borders (e.g. the...
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Journal of Student-Scientists' Research
2023
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Online Access: | https://dx.doi.org/10.13021/jssr2023.4002 https://journals.gmu.edu/index.php/jssr/article/view/4002 |
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ftdatacite:10.13021/jssr2023.4002 2023-12-03T10:30:08+01:00 ArcCI Development ... PATRICK O’BRIEN ABISHEK KANTHAN Spanbauer, Theodore Hollar, Grady Chaowei Yang 2023 https://dx.doi.org/10.13021/jssr2023.4002 https://journals.gmu.edu/index.php/jssr/article/view/4002 en eng Journal of Student-Scientists' Research https://dx.doi.org/10.13021/jssr.2023 Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 ScholarlyArticle Text article-journal Article 2023 ftdatacite https://doi.org/10.13021/jssr2023.400210.13021/jssr.2023 2023-11-03T11:13:07Z Sea ice is important to study as it reflects climate change and the state of the environment. ArcCI is a utility which allows users to upload images of sea ice from online databases which can be cropped into smaller resolutions, which are segmented using a variety of algorithms via borders (e.g. the border between ice and water). The user can then categorize each segment as a different property (e.g. ice or water). Most standard methods have difficulty determining the difference between melt ponds and submerged ice, but through Polygon Neighbor Analysis, the regions surrounding a body of water with ice underneath can be used to determine the feature. This method can only be implemented with HSR (high resolution images), which are much larger in size and therefore more challenging to optimally process. The user interface is being updated alongside the development of a desktop variant of the app. Additionally, an artificial intelligence model is being trained by interns and the ArcCI development team to ... : Journal of Student-Scientists' Research, Vol. 5 (2023) ... Article in Journal/Newspaper Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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English |
description |
Sea ice is important to study as it reflects climate change and the state of the environment. ArcCI is a utility which allows users to upload images of sea ice from online databases which can be cropped into smaller resolutions, which are segmented using a variety of algorithms via borders (e.g. the border between ice and water). The user can then categorize each segment as a different property (e.g. ice or water). Most standard methods have difficulty determining the difference between melt ponds and submerged ice, but through Polygon Neighbor Analysis, the regions surrounding a body of water with ice underneath can be used to determine the feature. This method can only be implemented with HSR (high resolution images), which are much larger in size and therefore more challenging to optimally process. The user interface is being updated alongside the development of a desktop variant of the app. Additionally, an artificial intelligence model is being trained by interns and the ArcCI development team to ... : Journal of Student-Scientists' Research, Vol. 5 (2023) ... |
format |
Article in Journal/Newspaper |
author |
PATRICK O’BRIEN ABISHEK KANTHAN Spanbauer, Theodore Hollar, Grady Chaowei Yang |
spellingShingle |
PATRICK O’BRIEN ABISHEK KANTHAN Spanbauer, Theodore Hollar, Grady Chaowei Yang ArcCI Development ... |
author_facet |
PATRICK O’BRIEN ABISHEK KANTHAN Spanbauer, Theodore Hollar, Grady Chaowei Yang |
author_sort |
PATRICK O’BRIEN |
title |
ArcCI Development ... |
title_short |
ArcCI Development ... |
title_full |
ArcCI Development ... |
title_fullStr |
ArcCI Development ... |
title_full_unstemmed |
ArcCI Development ... |
title_sort |
arcci development ... |
publisher |
Journal of Student-Scientists' Research |
publishDate |
2023 |
url |
https://dx.doi.org/10.13021/jssr2023.4002 https://journals.gmu.edu/index.php/jssr/article/view/4002 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.13021/jssr.2023 |
op_rights |
Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 |
op_doi |
https://doi.org/10.13021/jssr2023.400210.13021/jssr.2023 |
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1784255811094052864 |