Ready-To-Train AI4Arctic Sea Ice Challenge Test Dataset ...

• Main Authors: Buus-Hinkler, Jørgen, Wulf, Tore, Stokholm, Andreas Rønne, Korosov, Anton, Saldo, Roberto, Pedersen, Leif Toudal, Arthurs, David, Solberg, Rune, Longépé, Nicolas, Brandt Kreiner, Matilde
• Format: Dataset
• Language: unknown
• Published: Technical University of Denmark 2022
• Subjects: Other environmental sciences not elsewhere classified; Greenland; Sea ice
• Online Access: https://dx.doi.org/10.11583/dtu.21762830.v1; https://data.dtu.dk/articles/dataset/Ready-To-Train_AI4Arctic_Sea_Ice_Challenge_Test_Dataset/21762830/1

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 ...