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The Innovation Informatics > 2026 Vol. 2 > No. 1 > 100022
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Toward minute-level flood stereoscopic intelligence: Real-time multimodal remote sensing under an on-orbit AI paradigm

  • 1.

    Academy of Ecological Civilization Development for JING-JIN-JI Megalopolis, Tianjin Normal University, Tianjin 300387, China

  • 2.

    School of Astronautics, Research Center of Satellite Technology, Harbin Institute of Technology, Harbin 150006, China

  • 3.

    Advanced Interdisciplinary Institute of Satellite Applications, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • 4.

    Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, San Diego, California 92037, USA

  • 5.

    Key Laboratory for Resource Use and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

  • 6.

    Institute of Hydraulics and Ocean Engineering, Ningbo University, Ningbo 315000, China

  • 7.

    College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China

  • 8.

    Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519000, China

  • 9.

    Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

  • 10.

    MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China

  • 11.

    School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China

  • 12.

    These authors contributed equally

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  • Corresponding author: xunan2025@szu.edu.cn
  • Received Date:  December 03, 2025
    Accepted Date:  December 31, 2025
    Published Online:  January 08, 2026
The Innovation Informatics  2(1),  https://doi.org/10.59717/j.xinn-inform.2026.100022  |  Cite this article
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    • REFERENCES
  • [1] Misra A., White K., Nsutezo S.F., et al. (2025). Mapping global floods with 10 years of satellite radar data. Nat. Commun. 16:5762. DOI:10.1038/s41467-025-60973-1

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    [2] Bentivoglio R., Isufi E., Jonkman S.N., et al. (2022). Deep learning methods for flood mapping: A review of existing applications and future research directions. Hydrol. Earth Syst. Sci. 26:4345−4378. DOI:10.5194/hess-26-4345-2022

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    [3] Wang Q. (2022). Research framework of remote sensing monitoring and real-time diagnosis of earth surface anomalies. Acta Geod. Cartogr. Sin. 51:11. DOI:10.11947/j.AGCS.2022.20220124

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    [4] Li D., Wang M., Yang F., et al. (2023). Internet intelligent remote sensing scientific experimental satellite Luojia-3-01. Geo-spat. Inf. Sci. 26:257−261. DOI:10.1080/10095020.2023.2208472

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    [5] Yao J., Wang M., Xu N., et al. (2025). 3-D flood mapping from SWOT observations during extreme rainfall: A case study of Gangnan Reservoir. Int. J. Digit. Earth. 18:2544916. DOI:10.1080/17538947.2025.2544916

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  • Cite this article:

    Yao J., Ma C., Wang M., et al. (2026). Toward minute-level flood stereoscopic intelligence: Real-time multimodal remote sensing under an on-orbit AI paradigm. The Innovation Informatics 2:100022. https://doi.org/10.59717/j.xinn-inform.2026.100022
    Yao J., Ma C., Wang M., et al. (2026). Toward minute-level flood stereoscopic intelligence: Real-time multimodal remote sensing under an on-orbit AI paradigm. The Innovation Informatics 2:100022. https://doi.org/10.59717/j.xinn-inform.2026.100022

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