Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1621-2024
https://doi.org/10.5194/tc-18-1621-2024
Research article
 | 
08 Apr 2024
Research article |  | 08 Apr 2024

MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model

Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi

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Latest update: 20 Jul 2024
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Short summary
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.