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Learning the unobservable sea: Spatial-temporal foundation models as the next frontier of Earth-system intelligence

  • 1.

    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA

  • 3.

    College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

  • 4.

    Department of Electronic Engineering, BNRist, Tsinghua University, Beijing 100084, China

  • 5.

    University of Chinese Academy of Sciences, Beijing 100049, China

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  • Corresponding author: wangfei@ict.ac.cn (F.W.)
  • Received Date:  September 04, 2026
    Accepted Date:  September 20, 2026
    Published Online:  September 24, 2026
AI Plus  1(1),  https://doi.org/10.59717/ipj.aiplus.2026.100012  |  Cite this article
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  • Cite this article:

    Xu B., Yuan Y., Fan C., et al. (2026). Learning the unobservable sea: Spatial-temporal foundation models as the next frontier of Earth-system intelligence. AI Plus 1:100012. https://doi.org/10.59717/ipj.aiplus.2026.100012
    Xu B., Yuan Y., Fan C., et al. (2026). Learning the unobservable sea: Spatial-temporal foundation models as the next frontier of Earth-system intelligence. AI Plus 1:100012. https://doi.org/10.59717/ipj.aiplus.2026.100012

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