Articles | Volume 18, issue 4
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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Cited articles

Boulze, H., Korosov, A., and Brajard, J.: Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks, Remote Sens., 12, 2165,, 2020. a
Buus-Hinkler, J., Wulf, T., Stokholm, A. R., Korosov, A., Saldo, R., Pedersen, L. T., Arthurs, D., Solberg, R., Longépé, N., and Brandt Kreiner, M.: AI4Arctic Sea Ice Challenge Dataset, DTU [code and data set],, 2022. a, b, c
Chen, S., Shokr, M., Li, X., Ye, Y., Zhang, Z., Hui, F., and Cheng, X.: MYI floes identification based on the texture and shape feature from dual-polarized Sentinel-1 imagery, Remote Sens., 12, 3221, 2020. a
Chen, X., Scott, K. A., Jiang, M., Fang, Y., Xu, L., and Clausi, D. A.: Sea Ice Classification With Dual-Polarized SAR Imagery: A Hierarchical Pipeline, in: Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., Waikoloa, USA, January, 2023, 224–232, 2023a. a
Chen, X., Valencia, R., Soleymani, A., and Scott, K. A.: Predicting Sea Ice Concentration With Uncertainty Quantification Using Passive Microwave and Reanalysis Data: A Case Study in Baffin Bay, IEEE Trans. Geosci. Remote Sens., 61, 1–13,, 2023b. a, b
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.