Raw 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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Online Access: | https://doi.org/10.11583/dtu.21284967.v2 |
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ftunivfreestate:oai:figshare.com:article/21284967 2023-05-15T15:17:52+02:00 Raw AI4Arctic Sea Ice Challenge Dataset Jørgen Buus-Hinkler (14147325) Tore Wulf (13915342) Andreas Rønne Stokholm (10315433) Anton Korosov (13915364) Roberto Saldo (6212717) Leif Toudal Pedersen (5352251) David Arthurs (14024199) Rune Solberg (14024201) Nicolas Longépé (14016975) Matilde Brandt Kreiner (14017008) 2022-11-21T12:49:02Z https://doi.org/10.11583/dtu.21284967.v2 unknown https://figshare.com/articles/dataset/Raw_AI4Arctic_Sea_Ice_Challenge_Dataset/21284967 doi:10.11583/dtu.21284967.v2 CC BY 4.0 CC-BY Other earth sciences not elsewhere classified sea ice Sentinel 1 Synthetic aperture radar (SAR) microwave radiometry Arctic research Cryosphere deep learning dataset Dataset 2022 ftunivfreestate https://doi.org/10.11583/dtu.21284967.v2 2022-12-30T00:23:53Z 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 Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the ' raw' and ' ready-to-train'- versions with corresponding test datasets . The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘ ready-to-train’ -version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the ' raw' -version . The netCDF files are bundled together in groups ~25 with the filename format corresponding to the Sentinel-1 satellite from which the SAR image was acquired by, followed by the first file acquisition time to the last, i.e. ... Dataset Arctic Greenland Sea ice KovsieScholar Repository (University of the Free State - UFS UV) Arctic Greenland The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) |
institution |
Open Polar |
collection |
KovsieScholar Repository (University of the Free State - UFS UV) |
op_collection_id |
ftunivfreestate |
language |
unknown |
topic |
Other earth sciences not elsewhere classified sea ice Sentinel 1 Synthetic aperture radar (SAR) microwave radiometry Arctic research Cryosphere deep learning dataset |
spellingShingle |
Other earth sciences not elsewhere classified sea ice Sentinel 1 Synthetic aperture radar (SAR) microwave radiometry Arctic research Cryosphere deep learning dataset Jørgen Buus-Hinkler (14147325) Tore Wulf (13915342) Andreas Rønne Stokholm (10315433) Anton Korosov (13915364) Roberto Saldo (6212717) Leif Toudal Pedersen (5352251) David Arthurs (14024199) Rune Solberg (14024201) Nicolas Longépé (14016975) Matilde Brandt Kreiner (14017008) Raw AI4Arctic Sea Ice Challenge Dataset |
topic_facet |
Other earth sciences not elsewhere classified sea ice Sentinel 1 Synthetic aperture radar (SAR) microwave radiometry Arctic research Cryosphere deep learning dataset |
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 Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the ' raw' and ' ready-to-train'- versions with corresponding test datasets . The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘ ready-to-train’ -version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the ' raw' -version . The netCDF files are bundled together in groups ~25 with the filename format corresponding to the Sentinel-1 satellite from which the SAR image was acquired by, followed by the first file acquisition time to the last, i.e. ... |
format |
Dataset |
author |
Jørgen Buus-Hinkler (14147325) Tore Wulf (13915342) Andreas Rønne Stokholm (10315433) Anton Korosov (13915364) Roberto Saldo (6212717) Leif Toudal Pedersen (5352251) David Arthurs (14024199) Rune Solberg (14024201) Nicolas Longépé (14016975) Matilde Brandt Kreiner (14017008) |
author_facet |
Jørgen Buus-Hinkler (14147325) Tore Wulf (13915342) Andreas Rønne Stokholm (10315433) Anton Korosov (13915364) Roberto Saldo (6212717) Leif Toudal Pedersen (5352251) David Arthurs (14024199) Rune Solberg (14024201) Nicolas Longépé (14016975) Matilde Brandt Kreiner (14017008) |
author_sort |
Jørgen Buus-Hinkler (14147325) |
title |
Raw AI4Arctic Sea Ice Challenge Dataset |
title_short |
Raw AI4Arctic Sea Ice Challenge Dataset |
title_full |
Raw AI4Arctic Sea Ice Challenge Dataset |
title_fullStr |
Raw AI4Arctic Sea Ice Challenge Dataset |
title_full_unstemmed |
Raw AI4Arctic Sea Ice Challenge Dataset |
title_sort |
raw ai4arctic sea ice challenge dataset |
publishDate |
2022 |
url |
https://doi.org/10.11583/dtu.21284967.v2 |
long_lat |
ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
Arctic Greenland The Sentinel |
geographic_facet |
Arctic Greenland The Sentinel |
genre |
Arctic Greenland Sea ice |
genre_facet |
Arctic Greenland Sea ice |
op_relation |
https://figshare.com/articles/dataset/Raw_AI4Arctic_Sea_Ice_Challenge_Dataset/21284967 doi:10.11583/dtu.21284967.v2 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.11583/dtu.21284967.v2 |
_version_ |
1766348119134437376 |