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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Total article views: 2,967 (including HTML, PDF, and XML)
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Total article views: 3,110 (including HTML, PDF, and XML)
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Cited
21 citations as recorded by crossref.
- Performance and generalizability impacts of incorporating location encoders into deep learning for dynamic PM 2.5 estimation M. Karimzadeh et al. https://doi.org/10.1080/15481603.2025.2594797
- Cloud computing-based freshwater ice mapping using synthetic aperture radar imagery C. Chaabani et al. https://doi.org/10.1007/s12145-025-01892-z
- MFDA: Unified Multi-Task Architecture for Cross-Scene Sea Ice Classification Y. Chen et al. https://doi.org/10.1109/TGRS.2024.3491190
- AGL-UNet: Adaptive Global–Local Modulated U-Net for Multitask Sea Ice Mapping D. Chen & F. Zheng https://doi.org/10.3390/s26030959
- Region-wise query-guided adaptive multimodal fusion network for fine-grained arctic sea ice recognition T. Ma et al. https://doi.org/10.1080/17538947.2026.2658300
- A Conditional Denoising Diffusion Probabilistic Model for Sea Ice Concentration Estimation Y. Chen et al. https://doi.org/10.1109/LGRS.2025.3606975
- A comparative study of data input selection for deep learning-based automated sea ice mapping X. Chen et al. https://doi.org/10.1016/j.jag.2024.103920
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al. https://doi.org/10.1016/j.rsase.2025.101538
- A Dual-Branch Architecture for Adaptive Loss Multitask Mapping Based on AI4Arctic Sea Ice Challenge Dataset T. Feng et al. https://doi.org/10.1109/LGRS.2025.3528621
- Remote sensing-based sea ice concentration estimation via weighted deep learning networks M. Stofa & M. Zulkifley https://doi.org/10.1016/j.ecoinf.2026.103597
- A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset X. Chen et al. https://doi.org/10.1109/TGRS.2025.3542803
- An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al. https://doi.org/10.1109/JSTARS.2025.3636999
- Enhancing sea ice classification on SAR imagery by integrating texture and polarimetric information with a deep learning model L. Gao https://doi.org/10.1007/s00343-025-5218-6
- The AutoICE Challenge A. Stokholm et al. https://doi.org/10.5194/tc-18-3471-2024
- GLFFuse: A Multimodal Feature-Level Fusion Network for Multitask Fine-Grained Recognition of Arctic Sea Ice T. Ma et al. https://doi.org/10.1109/JSTARS.2026.3660828
- Sea Ice Classification Enhancement Using Calibration-Focused Loss Functions N. Ahmadian et al. https://doi.org/10.3390/rs18050810
- Mapping sea ice from spaceborne SAR imagery using a multi-modal spatial transformer M. Ziaja & J. Nalepa https://doi.org/10.1016/j.asr.2026.05.097
- Sea Ice and Water Segmentation in SAR Imagery Based on Polarization Channel Interaction and Edge Selective Fusion W. Song et al. https://doi.org/10.3390/rs18060945
- Pan-Arctic winter sea ice classification using Sentinel-1 dual-polarized SAR images Y. Dai et al. https://doi.org/10.1016/j.rse.2025.115140
- Sea ice concentration estimation via physical information-guided multi-source data fusion and spatial continuity preservation X. Liu et al. https://doi.org/10.1016/j.jag.2026.105339
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al. https://doi.org/10.1109/JSTARS.2025.3551976
21 citations as recorded by crossref.
- Performance and generalizability impacts of incorporating location encoders into deep learning for dynamic PM 2.5 estimation M. Karimzadeh et al. https://doi.org/10.1080/15481603.2025.2594797
- Cloud computing-based freshwater ice mapping using synthetic aperture radar imagery C. Chaabani et al. https://doi.org/10.1007/s12145-025-01892-z
- MFDA: Unified Multi-Task Architecture for Cross-Scene Sea Ice Classification Y. Chen et al. https://doi.org/10.1109/TGRS.2024.3491190
- AGL-UNet: Adaptive Global–Local Modulated U-Net for Multitask Sea Ice Mapping D. Chen & F. Zheng https://doi.org/10.3390/s26030959
- Region-wise query-guided adaptive multimodal fusion network for fine-grained arctic sea ice recognition T. Ma et al. https://doi.org/10.1080/17538947.2026.2658300
- A Conditional Denoising Diffusion Probabilistic Model for Sea Ice Concentration Estimation Y. Chen et al. https://doi.org/10.1109/LGRS.2025.3606975
- A comparative study of data input selection for deep learning-based automated sea ice mapping X. Chen et al. https://doi.org/10.1016/j.jag.2024.103920
- Enhancing and Interpreting Deep Learning for Sea Ice Charting using the AutoICE Benchmark S. Jalayer et al. https://doi.org/10.1016/j.rsase.2025.101538
- A Dual-Branch Architecture for Adaptive Loss Multitask Mapping Based on AI4Arctic Sea Ice Challenge Dataset T. Feng et al. https://doi.org/10.1109/LGRS.2025.3528621
- Remote sensing-based sea ice concentration estimation via weighted deep learning networks M. Stofa & M. Zulkifley https://doi.org/10.1016/j.ecoinf.2026.103597
- A Weakly Supervised Learning Approach for Sea Ice Stage of Development Classification From AI4Arctic Sea Ice Challenge Dataset X. Chen et al. https://doi.org/10.1109/TGRS.2025.3542803
- An Arctic Sea Ice Thickness Inversion Method Based on Deep Learning Two-Branch Architecture and Multisource Remote Sensing Data Fusion R. Huang et al. https://doi.org/10.1109/JSTARS.2025.3636999
- Enhancing sea ice classification on SAR imagery by integrating texture and polarimetric information with a deep learning model L. Gao https://doi.org/10.1007/s00343-025-5218-6
- The AutoICE Challenge A. Stokholm et al. https://doi.org/10.5194/tc-18-3471-2024
- GLFFuse: A Multimodal Feature-Level Fusion Network for Multitask Fine-Grained Recognition of Arctic Sea Ice T. Ma et al. https://doi.org/10.1109/JSTARS.2026.3660828
- Sea Ice Classification Enhancement Using Calibration-Focused Loss Functions N. Ahmadian et al. https://doi.org/10.3390/rs18050810
- Mapping sea ice from spaceborne SAR imagery using a multi-modal spatial transformer M. Ziaja & J. Nalepa https://doi.org/10.1016/j.asr.2026.05.097
- Sea Ice and Water Segmentation in SAR Imagery Based on Polarization Channel Interaction and Edge Selective Fusion W. Song et al. https://doi.org/10.3390/rs18060945
- Pan-Arctic winter sea ice classification using Sentinel-1 dual-polarized SAR images Y. Dai et al. https://doi.org/10.1016/j.rse.2025.115140
- Sea ice concentration estimation via physical information-guided multi-source data fusion and spatial continuity preservation X. Liu et al. https://doi.org/10.1016/j.jag.2026.105339
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al. https://doi.org/10.1109/JSTARS.2025.3551976
Saved (final revised paper)
Latest update: 17 Jul 2026
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