VIIRS Sea ice leads detections using a U-Net
Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly importa...
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ftzenodo:oai:zenodo.org:7046261 2024-09-15T18:34:06+00:00 VIIRS Sea ice leads detections using a U-Net Hoffman, Jay Ackerman, Steven Liu, Yinghui Key, Jeffrey McConnell, Iain 2022-09-02 https://doi.org/10.5061/dryad.1vhhmgqwd unknown Zenodo https://doi.org/10.3390/rs13224571 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.1vhhmgqwd oai:zenodo.org:7046261 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode leads sea ice sea ice leads Arctic Arctic sea ice U-Net Convolutional neural network info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5061/dryad.1vhhmgqwd10.3390/rs13224571 2024-07-26T03:53:07Z Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2011 for the season between November through April. The daily results are recorded as hdf5 format files. For each season, the daily results from November through April for each season are combined into a new tar file with gzip compression. Funding provided by: National Aeronautics and Space Administration Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000104 Award Number: 80NSSC18K0786 Other/Unknown Material Sea ice Zenodo |
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leads sea ice sea ice leads Arctic Arctic sea ice U-Net Convolutional neural network |
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leads sea ice sea ice leads Arctic Arctic sea ice U-Net Convolutional neural network Hoffman, Jay Ackerman, Steven Liu, Yinghui Key, Jeffrey McConnell, Iain VIIRS Sea ice leads detections using a U-Net |
topic_facet |
leads sea ice sea ice leads Arctic Arctic sea ice U-Net Convolutional neural network |
description |
Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2011 for the season between November through April. The daily results are recorded as hdf5 format files. For each season, the daily results from November through April for each season are combined into a new tar file with gzip compression. Funding provided by: National Aeronautics and Space Administration Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000104 Award Number: 80NSSC18K0786 |
format |
Other/Unknown Material |
author |
Hoffman, Jay Ackerman, Steven Liu, Yinghui Key, Jeffrey McConnell, Iain |
author_facet |
Hoffman, Jay Ackerman, Steven Liu, Yinghui Key, Jeffrey McConnell, Iain |
author_sort |
Hoffman, Jay |
title |
VIIRS Sea ice leads detections using a U-Net |
title_short |
VIIRS Sea ice leads detections using a U-Net |
title_full |
VIIRS Sea ice leads detections using a U-Net |
title_fullStr |
VIIRS Sea ice leads detections using a U-Net |
title_full_unstemmed |
VIIRS Sea ice leads detections using a U-Net |
title_sort |
viirs sea ice leads detections using a u-net |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5061/dryad.1vhhmgqwd |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://doi.org/10.3390/rs13224571 https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.1vhhmgqwd oai:zenodo.org:7046261 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.1vhhmgqwd10.3390/rs13224571 |
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1810475840429883392 |