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Ready-To-Train AI4Arctic Sea Ice Challenge Dataset

posted on 21.11.2022, 12:49 authored by Jørgen Buus-HinklerJørgen Buus-Hinkler, Tore Wulf, Andreas Rønne StokholmAndreas Rønne Stokholm, Anton Korosov, Roberto SaldoRoberto Saldo, Leif Toudal PedersenLeif Toudal Pedersen, David Arthurs, Rune Solberg, Nicolas Longépé, Matilde Brandt Kreiner

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 493 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 Ready-To-Train version.

Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”.

Code with a get-started toolkit for the 'ready-to-train' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge

A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg 

This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065 


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