The AutoICE Challenge

• Published in: The Cryosphere
• Main Authors: Stokholm, Andreas, Buus-Hinkler, Jørgen, Wulf, Tore, Korosov, Anton, Saldo, Roberto, Pedersen, Leif Toudal, Arthurs, David, Dragan, Ionut, Modica, Iacopo, Pedro, Juan, Debien, Annekatrien, Chen, Xinwei, Patel, Muhammed, Cantu, Fernando Jose Pena, Turnes, Javier Noa, Park, Jinman, Xu, Linlin, Scott, Katharine Andrea, Clausi, David Anthony, Fang, Yuan, Jiang, Mingzhe, Taleghanidoozdoozan, Saeid, Brubacher, Neil Curtis, Soleymani, Armina, Gousseau, Zacharie, Smaczny, Michał, Kowalski, Patryk, Komorowski, Jacek, Rijlaarsdam, David, Van Rijn, Jan Nicolaas, Jakobsen, Jens, Rogers, Martin Samuel James, Hughes, Nick, Zagon, Tom, Solberg, Rune, Longépé, Nicolas, Kreiner, Matilde Brandt
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: Arctic; Sea ice
• Online Access: https://orbit.dtu.dk/en/publications/69324551-985c-4772-8850-94f987451515; https://doi.org/10.5194/tc-18-3471-2024; https://backend.orbit.dtu.dk/ws/files/372492597/tc-18-3471-2024.pdf

Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants' submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results.