Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network
International audience 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 obstructi...
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ftccsdartic:oai:HAL:hal-04414373v1 2024-02-11T10:01:09+01:00 Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network Wang, Haoran Wei, Jiawei Gao, Xiaoyong Sha, Dexuan Luo, Yiyun Yang, Junliu Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD France-Sud )-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM) Association for Computing Machinery Maria Lui Damiani Matthias Renz Ahmed Eldawy Peer Kröger Hamburg, Germany 2023-11-13 https://hal.inrae.fr/hal-04414373 https://doi.org/10.1145/3589132.3629972 en eng HAL CCSD ACM (Association for Computing Machinery) info:eu-repo/semantics/altIdentifier/doi/10.1145/3589132.3629972 ISBN: 979-8-4007-0168-9 hal-04414373 https://hal.inrae.fr/hal-04414373 doi:10.1145/3589132.3629972 Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems https://hal.inrae.fr/hal-04414373 SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems, Association for Computing Machinery, Nov 2023, Hamburg, Germany. pp.1-4, ⟨10.1145/3589132.3629972⟩ https://dl.acm.org/doi/proceedings/10.1145/3589132 Deep learning Dual-attention Object detection Supraglacial lakes [SDE]Environmental Sciences info:eu-repo/semantics/conferenceObject Conference papers 2023 ftccsdartic https://doi.org/10.1145/3589132.362997210.1145/3589132 2024-01-27T23:58:41Z International audience 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. Conference Object Arctic Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Arctic |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
language |
English |
topic |
Deep learning Dual-attention Object detection Supraglacial lakes [SDE]Environmental Sciences |
spellingShingle |
Deep learning Dual-attention Object detection Supraglacial lakes [SDE]Environmental Sciences Wang, Haoran Wei, Jiawei Gao, Xiaoyong Sha, Dexuan Luo, Yiyun Yang, Junliu Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network |
topic_facet |
Deep learning Dual-attention Object detection Supraglacial lakes [SDE]Environmental Sciences |
description |
International audience 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. |
author2 |
Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD France-Sud )-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM) Association for Computing Machinery Maria Lui Damiani Matthias Renz Ahmed Eldawy Peer Kröger |
format |
Conference Object |
author |
Wang, Haoran Wei, Jiawei Gao, Xiaoyong Sha, Dexuan Luo, Yiyun Yang, Junliu |
author_facet |
Wang, Haoran Wei, Jiawei Gao, Xiaoyong Sha, Dexuan Luo, Yiyun Yang, Junliu |
author_sort |
Wang, Haoran |
title |
Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network |
title_short |
Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network |
title_full |
Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network |
title_fullStr |
Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network |
title_full_unstemmed |
Enhanced Mapping of Supraglacial Lakes Through Dual-attention Deep Neural Network |
title_sort |
enhanced mapping of supraglacial lakes through dual-attention deep neural network |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.inrae.fr/hal-04414373 https://doi.org/10.1145/3589132.3629972 |
op_coverage |
Hamburg, Germany |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems https://hal.inrae.fr/hal-04414373 SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems, Association for Computing Machinery, Nov 2023, Hamburg, Germany. pp.1-4, ⟨10.1145/3589132.3629972⟩ https://dl.acm.org/doi/proceedings/10.1145/3589132 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1145/3589132.3629972 ISBN: 979-8-4007-0168-9 hal-04414373 https://hal.inrae.fr/hal-04414373 doi:10.1145/3589132.3629972 |
op_doi |
https://doi.org/10.1145/3589132.362997210.1145/3589132 |
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1790596899437281280 |