Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-1621-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/tc-18-1621-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Apr 2024
Research article |
| 08 Apr 2024
| 08 Apr 2024
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Xinwei Chen
School of Marine Science and Engineering, South China University of Technology, Guangzhou, China
Muhammed Patel
Vision and Image Processing Lab, Department of System Design Engineering, University of Waterloo, Waterloo, ON, Canada
Fernando J. Pena Cantu
Vision and Image Processing Lab, Department of System Design Engineering, University of Waterloo, Waterloo, ON, Canada
Jinman Park
Vision and Image Processing Lab, Department of System Design Engineering, University of Waterloo, Waterloo, ON, Canada
Javier Noa Turnes
Vision and Image Processing Lab, Department of System Design Engineering, University of Waterloo, Waterloo, ON, Canada
Linlin Xu
CORRESPONDING AUTHOR
Vision and Image Processing Lab, Department of System Design Engineering, University of Waterloo, Waterloo, ON, Canada
K. Andrea Scott
Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada
David A. Clausi
Vision and Image Processing Lab, Department of System Design Engineering, University of Waterloo, Waterloo, ON, Canada
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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.
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net...
Special issue