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Ringmo-SenseV2: Remote Sensing Foundation Model for Spatiotemporal Prediction Based on Multisource Heterogeneous Time-Series Data

Liangyu Xu,Wanxuan Lu,Lieyi Hu,Heming Yang,Yi Jiang,Chenglong Liu,Hongfeng Yu,Chubo Deng,Xian Sun,Kun Fu

Published 2025 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

The rapid development of remote sensing (RS) technology has generated a vast amount of heterogeneous time-series data from various sources, including drone videos, satellite time-series images, and multiobject trajectories. Effectively processing and analyzing this multisource heterogeneous data for accurate spatiotemporal prediction is crucial in fields such as environmental protection and disaster response. In this article, we propose a universal predictive foundation model named Ringmo-SenseV2 to learn the general evolutionary patterns of RS elements from massive heterogeneous data. Ringmo-SenseV2 features a mixture-of-heterogeneous-experts (MoHE) Transformer, which unifies the modeling of multisource heterogeneous time-series data. Additionally, to better capture the complex dependencies across different spatiotemporal locations, we introduce a hypergraph translator (HT), treating embeddings of different spatiotemporal locations as nodes and employing hypergraph convolution for information propagation. Furthermore, to enhance the model’s adaptability to different evolution speeds during pretraining, we implement the adaptive tube masking (AM) strategy, which controls prediction difficulty by adaptively setting mask proportions for sequences with varying evolution speeds. Extensive experiments demonstrate that Ringmo-SenseV2 exhibits outstanding performance across various RS prediction tasks. Further tests on scene graph generation for RS images showcase the model’s ability to extract image features, thereby enhancing image perception tasks.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Transactions on Geoscience and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/TGRS.2025.3565203
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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