11 Aug 2023
| 11 Aug 2023
Historical maps inform landform cognition in machine learning
Samantha T. Arundel
U.S. Geological Survey, USA
Gaurav Sinha
Ohio University, USA
Wenwen Li
Arizona State University, USA
David P. Martin
Duke Energy, USA
Kevin G. McKeehan
U.S. Geological Survey, USA
Philip T. Thiem
U.S. Geological Survey, USA
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Preprint withdrawn
Short summary
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.
L. V. Stanislawski, E. J. Shavers, A. J. Duffy, P. Thiem, N. Jaroenchai, S. Wang, Z. Jiang, B. J. Kronenfeld, and B. P. Buttenfield
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 449–456, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022, 2022
Samantha T. Arundel, Trenton P. Morgan, and Dennis P. Powon
Abstr. Int. Cartogr. Assoc., 3, 12, https://doi.org/10.5194/ica-abs-3-12-2021, https://doi.org/10.5194/ica-abs-3-12-2021, 2021
