Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network

Haoran Wang; Jiawei Wei; Xiaoyong Gao; Dexuan Sha; Yiyun Luo; Junliu Yang

doi:10.1145/3589132.3629972

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Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network

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Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network

Haoran Wang, Jiawei Wei, Xiaoyong Gao, Dexuan Sha, Yiyun Luo et Junliu Yang

Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pp.1-4

SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems (Hamburg, Germany, 13/11/2023–16/11/2023)

DOI: https://doi.org/10.1145/3589132.3629972

Résumé

Deep learning

Dual-attention

Object detection

Supraglacial lakes

Supraglacial lakes in the Arctic undergo seasonal and glacial-activity-induced changes, providing profound insights into ice dynamics and climate changes in these sensitive regions. However, the morphological complexity of these lakes, compounded by the environmental obstructions like clouds and slush fields, poses significant challenges to accurate lake detection. The 31st ACM SIGSPATIAL 2023 initiated a competition, GISCUP 2023, focusing on supraglacial lake detection based on multipart, multi-temporal satellite imagery. This paper, distinguished as the 3rd place winner, introduces a pioneering dual-attention U-net algorithm. This approach synergizes deep learning with spectral and spatial knowledge, ensuring a streamlined pipeline structure that upholds methodological soundness and yields satisfying results.

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Détails

Titre: Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network
Créateurs - sans rôle: Haoran Wang - National University of Singapore
Jiawei Wei - Nanjing Institute of Geography and Limnology
Xiaoyong Gao - National University of Singapore
Dexuan Sha
Yiyun Luo - Aix-Marseille Université
Junliu Yang - Université de Montpellier, BotAnique et Modélisation de l'Architecture des Plantes et des végétations - AMAP
Contributeurs - sans rôle: Maria Lui Damiani
Matthias Renz
Ahmed Eldawy
Peer Kröger
Détails de publication: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pp.1-4
Colloque: SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems (Hamburg, Germany, 13/11/2023–16/11/2023)
Identifiants: 9942758409311
Unité académique: BotAnique et Modélisation de l'Architecture des Plantes et des végétations - AMAP
Langue: English
Type de ressource: Conference proceeding
Champs locaux: hal-04414373