Rapid Flood Mapping: Outcome of the 2024 IEEE GRSS Data Fusion Contest [* Cross-Reference *]

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Li, Jiepan; Huang, He; He, Wei; Zhang, Hongyan; Zhang, Liangpei; Liu, Ting; Yuan, Mengke; Lu, Chaoran; Lu, Kaixuan; Peng, Baochai; Duan, Heyang; Li, Mengya; Zhang, Pan; Wang, Tao; Liao, Tongkui; Li, Yansheng; Dang, Bo; Wei, Fanyi; Tan, Jieyi; Lin, Yangjie; Persello, Claudio; Prasad, Saurabh; Vivone, Gemine; Lonjou, Vincent; Bretar, Frederic; Rodriguez-Suquet, Raquel; Guntzburger, Pauline; Poulain, Vincent; Le Moigne, Jacqueline; Smith, Benjamin; Kumar, Sujay; Huang, Thomas; Ricci, Sophie; Nguyen, Thanh Huy; Piacentini, Andrea, 2026, "Rapid Flood Mapping: Outcome of the 2024 IEEE GRSS Data Fusion Contest [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.Y1TTZC, LIST Open Repository, V1
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Description
This article presents the scientific outcomes of the 2024 Data Fusion Contest (DFC24) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, the Space for Climate Observatory, the Centre national d'etudes spatiales, the National Aeronautics and Space Administration, and the Centre Européen de Recherche et de Formation Avancée et Calcul Scientifique. The contest aims to advance image analysis and data fusion algorithms that generate reliable flood maps from multimodal Earth observation imagery. The DFC24 provides a large-scale, multimodal flood mapping benchmarking dataset and comprises two challenging competition tracks on the flood mapping task, one based on synthetic aperture radar imagery, and another using passive-optical imagery. Additional features, such as a digital terrain model and land-use and water occurrence, are also provided to the participants. This article presents the methods and results obtained by the first and second-ranked teams of each track. During the development phase, 1935 people registered for the contest, while at the end, 46 for Track 1 and 52 for Track 2 teams competed during the test phase in the two tracks, respectively. The data of this contest are openly available to the community for further research, development, and refinement of geospatial artificial intelligence, data fusion, and flood mapping methods. (2026-01-01)
***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1109/JSTARS.2026.3652462 for the original and latest version of the dataset and data downloads*** (2026-06-04)
Subject Earth and Environmental Sciences
Keyword Convolutional neural networks (CNNs), data fusion, deep learning, flood mapping, remote sensing (RS), transformers
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