A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ...
Data are available at: arcticdata.io/data/10.18739/A2N29P78F Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere and is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of Global climate processes and still not well und...
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NSF Arctic Data Center
2023
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Online Access: | https://dx.doi.org/10.18739/a2n29p78f https://arcticdata.io/catalog/view/doi:10.18739/A2N29P78F |
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ftdatacite:10.18739/a2n29p78f 2023-05-15T15:05:24+02:00 A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... Demchev, Denis Sudakow, Ivan Lyakhov, Dmitry Abramova, Irina Michels, Dominik Khodos, Alexander Kharchenko, Viktoria 2023 text/xml https://dx.doi.org/10.18739/a2n29p78f https://arcticdata.io/catalog/view/doi:10.18739/A2N29P78F en eng NSF Arctic Data Center Tundra lake SAR machine learining Arctic Yamal Alaska Sentinel-1 dataset Dataset 2023 ftdatacite https://doi.org/10.18739/a2n29p78f 2023-04-03T14:03:16Z Data are available at: arcticdata.io/data/10.18739/A2N29P78F Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere and is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of Global climate processes and still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) are a great source for tundra lakes recognition and their changes monitoring. However, manual analysis of their boundaries can be slow and inefficient, therefore reliable automated algorithms are required. This dataset aimed to fill the gap of the ground truth satellite images for algorithms training and validation and contains synthetic aperture radar imagery of tundra lakes from Sentonel-1 complemented with manually labeled masks of the lakes. The dataset covers two test sites in Yamal and Alaska areas for the summer months of 2015-2022. The images are generated for machine learning algorithms with a spatial resolution of 512x512 pixels. ... Dataset Arctic permafrost Tundra Alaska DataCite Metadata Store (German National Library of Science and Technology) Arctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Tundra lake SAR machine learining Arctic Yamal Alaska Sentinel-1 |
spellingShingle |
Tundra lake SAR machine learining Arctic Yamal Alaska Sentinel-1 Demchev, Denis Sudakow, Ivan Lyakhov, Dmitry Abramova, Irina Michels, Dominik Khodos, Alexander Kharchenko, Viktoria A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... |
topic_facet |
Tundra lake SAR machine learining Arctic Yamal Alaska Sentinel-1 |
description |
Data are available at: arcticdata.io/data/10.18739/A2N29P78F Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere and is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of Global climate processes and still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) are a great source for tundra lakes recognition and their changes monitoring. However, manual analysis of their boundaries can be slow and inefficient, therefore reliable automated algorithms are required. This dataset aimed to fill the gap of the ground truth satellite images for algorithms training and validation and contains synthetic aperture radar imagery of tundra lakes from Sentonel-1 complemented with manually labeled masks of the lakes. The dataset covers two test sites in Yamal and Alaska areas for the summer months of 2015-2022. The images are generated for machine learning algorithms with a spatial resolution of 512x512 pixels. ... |
format |
Dataset |
author |
Demchev, Denis Sudakow, Ivan Lyakhov, Dmitry Abramova, Irina Michels, Dominik Khodos, Alexander Kharchenko, Viktoria |
author_facet |
Demchev, Denis Sudakow, Ivan Lyakhov, Dmitry Abramova, Irina Michels, Dominik Khodos, Alexander Kharchenko, Viktoria |
author_sort |
Demchev, Denis |
title |
A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... |
title_short |
A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... |
title_full |
A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... |
title_fullStr |
A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... |
title_full_unstemmed |
A dataset of 512x512 tundra lakes imagery and binary masks from Sentinel-1 in the Yamal and Alaska areas, summer, 2015-2022 ... |
title_sort |
dataset of 512x512 tundra lakes imagery and binary masks from sentinel-1 in the yamal and alaska areas, summer, 2015-2022 ... |
publisher |
NSF Arctic Data Center |
publishDate |
2023 |
url |
https://dx.doi.org/10.18739/a2n29p78f https://arcticdata.io/catalog/view/doi:10.18739/A2N29P78F |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic permafrost Tundra Alaska |
genre_facet |
Arctic permafrost Tundra Alaska |
op_doi |
https://doi.org/10.18739/a2n29p78f |
_version_ |
1766337110920396800 |