Preprints
https://doi.org/10.5194/egusphere-2023-2831
https://doi.org/10.5194/egusphere-2023-2831
22 Jan 2024
 | 22 Jan 2024
Status: this preprint has been withdrawn by the authors.

Advancing Arctic sea ice remote sensing with AI and deep learning: now and future

Wenwen Li, Chia-Yu Hsu, and Marco Tedesco

Abstract. The revolutionary advances of Artificial Intelligence (AI) in the past decade have brought transformative innovation across science and engineering disciplines. Also in the field of Arctic science, we have witnessed an increasing trend in the adoption of AI, especially deep learning, to support the analysis of Arctic big data and facilitate new discoveries. In this paper, we provide a comprehensive review of the applications of deep learning in sea ice remote sensing domains, focusing on problems such as sea ice lead detection, thickness estimation, concentration, sea ice extent forecasting and motion detection as well as sea ice type classification. In addition to discussing these applications, we also summarize technological advances that provide customized deep learning solutions, including new loss functions and learning strategies to better understand sea ice dynamics. To promote the growth of this exciting interdisciplinary field, we further explore several research areas where the Arctic sea ice community can benefit from cutting-edge AI technology. These areas include improving multi-modal deep learning capabilities, enhancing model accuracy in measuring prediction uncertainty, better leveraging AI foundation models, and deepening the integration with physics-based models. We hope that this paper can serve as a cornerstone in the progress of Arctic sea ice research using AI and inspire further advances in this field.

This preprint has been withdrawn.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Wenwen Li, Chia-Yu Hsu, and Marco Tedesco

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2023-2831', Andrew Shepherd, 24 Jan 2024
    • AC1: 'Reply on CC1', Wenwen Li, 31 Jan 2024
  • RC1: 'Comment on egusphere-2023-2831', Anonymous Referee #1, 13 Mar 2024
  • RC2: 'Comment on egusphere-2023-2831', Anonymous Referee #2, 13 Mar 2024
    • AC2: 'Reply on RC2', Wenwen Li, 15 Mar 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2023-2831', Andrew Shepherd, 24 Jan 2024
    • AC1: 'Reply on CC1', Wenwen Li, 31 Jan 2024
  • RC1: 'Comment on egusphere-2023-2831', Anonymous Referee #1, 13 Mar 2024
  • RC2: 'Comment on egusphere-2023-2831', Anonymous Referee #2, 13 Mar 2024
    • AC2: 'Reply on RC2', Wenwen Li, 15 Mar 2024
Wenwen Li, Chia-Yu Hsu, and Marco Tedesco
Wenwen Li, Chia-Yu Hsu, and Marco Tedesco

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Short summary
This review paper fills a knowledge gap in comprehensive literature review at the junction of AI-Arctic sea ice research. We provide a fine-grained review of AI applications in a variety of sea ice research domains. Based on these analyses, we point out exciting opportunities where the Arctic sea ice community can continue benefiting from cutting-edge AI. These future research directions will foster the continuous growth of the Arctic sea ice–AI research community.