Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ...
The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development...
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Technical University of Denmark
2023
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Online Access: | https://dx.doi.org/10.11583/dtu.21316608 https://data.dtu.dk/articles/dataset/Ready-To-Train_AI4Arctic_Sea_Ice_Challenge_Dataset/21316608 |
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ftdatacite:10.11583/dtu.21316608 2023-08-27T04:09:46+02:00 Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... Buus-Hinkler, Jørgen Wulf, Tore Stokholm, Andreas Rønne Korosov, Anton Saldo, Roberto Pedersen, Leif Toudal Arthurs, David Solberg, Rune Longépé, Nicolas Brandt Kreiner, Matilde 2023 https://dx.doi.org/10.11583/dtu.21316608 https://data.dtu.dk/articles/dataset/Ready-To-Train_AI4Arctic_Sea_Ice_Challenge_Dataset/21316608 unknown Technical University of Denmark https://doi.org/10.11583/DTU.c.6244065 https://doi.org/10.11583/DTU.c.6244065 https://dx.doi.org/10.1109/tgrs.2022.3149323 https://dx.doi.org/10.1109/tgrs.2020.3004539 https://dx.doi.org/10.11583/dtu.13011134.v3 https://dx.doi.org/10.11583/dtu.11920416.v1 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Other earth sciences not elsewhere classified dataset Dataset 2023 ftdatacite https://doi.org/10.11583/dtu.2131660810.1109/tgrs.2022.314932310.1109/tgrs.2020.300453910.11583/dtu.13011134.v310.11583/dtu.11920416.v1 2023-08-07T08:40:18Z The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological ... Dataset Greenland Sea ice DataCite Metadata Store (German National Library of Science and Technology) Greenland |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Other earth sciences not elsewhere classified |
spellingShingle |
Other earth sciences not elsewhere classified Buus-Hinkler, Jørgen Wulf, Tore Stokholm, Andreas Rønne Korosov, Anton Saldo, Roberto Pedersen, Leif Toudal Arthurs, David Solberg, Rune Longépé, Nicolas Brandt Kreiner, Matilde Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... |
topic_facet |
Other earth sciences not elsewhere classified |
description |
The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological ... |
format |
Dataset |
author |
Buus-Hinkler, Jørgen Wulf, Tore Stokholm, Andreas Rønne Korosov, Anton Saldo, Roberto Pedersen, Leif Toudal Arthurs, David Solberg, Rune Longépé, Nicolas Brandt Kreiner, Matilde |
author_facet |
Buus-Hinkler, Jørgen Wulf, Tore Stokholm, Andreas Rønne Korosov, Anton Saldo, Roberto Pedersen, Leif Toudal Arthurs, David Solberg, Rune Longépé, Nicolas Brandt Kreiner, Matilde |
author_sort |
Buus-Hinkler, Jørgen |
title |
Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... |
title_short |
Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... |
title_full |
Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... |
title_fullStr |
Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... |
title_full_unstemmed |
Ready-To-Train AI4Arctic Sea Ice Challenge Dataset ... |
title_sort |
ready-to-train ai4arctic sea ice challenge dataset ... |
publisher |
Technical University of Denmark |
publishDate |
2023 |
url |
https://dx.doi.org/10.11583/dtu.21316608 https://data.dtu.dk/articles/dataset/Ready-To-Train_AI4Arctic_Sea_Ice_Challenge_Dataset/21316608 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Sea ice |
genre_facet |
Greenland Sea ice |
op_relation |
https://doi.org/10.11583/DTU.c.6244065 https://doi.org/10.11583/DTU.c.6244065 https://dx.doi.org/10.1109/tgrs.2022.3149323 https://dx.doi.org/10.1109/tgrs.2020.3004539 https://dx.doi.org/10.11583/dtu.13011134.v3 https://dx.doi.org/10.11583/dtu.11920416.v1 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.11583/dtu.2131660810.1109/tgrs.2022.314932310.1109/tgrs.2020.300453910.11583/dtu.13011134.v310.11583/dtu.11920416.v1 |
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