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XU Wenjia, YU Ruiqing, XUE Minghao, et al. A survey on remote sensing multimodal large language models: Framework, core technologies, and future perspectives[J]. Journal of Radars, in press. doi: 10.12000/JR25088
Citation: XU Wenjia, YU Ruiqing, XUE Minghao, et al. A survey on remote sensing multimodal large language models: Framework, core technologies, and future perspectives[J]. Journal of Radars, in press. doi: 10.12000/JR25088
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A Survey on Remote Sensing Multimodal Large Language Models: Framework, Core Technologies, and Future Perspectives

DOI: 10.12000/JR25088 CSTR: 32380.14.JR25088
  • 1.

    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100081, China

  • 2.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100091, China

  • 3.

    School of Geographical Sciences, Hunan Normal University, Changsha 410081, China

  • 4.

    Suzhou Aerospace Information Research Institute, Suzhou 215123, China

  • 5.

    National Key Laboratory of Microwave Imaging, Beijing 100190, China

  • 6.

    School of Electionic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China

Funds:  The National Natural Science Fundation of China (62301063), The Key Laboratory of Target Cognition and Application Technology (2023-CXPT-LC-005), The National Key Laboratory of Microwave Imaging Technology (70323006)
More Information
  • Corresponding author: XU Wenjia, xuwenjia@bupt.edu.cn
  • Received Date: 2025-05-12
  • Rev Recd Date: 2025-07-22
    • Available Online: 2025-07-27
    Abstract
  • In recent years, the rapid development of Multimodal Large Language Models (MLLMs) and their applications in remote sensing have garnered significant attention. Remote sensing MLLMs achieve deep integration of visual features and semantic information through the design of bridging mechanisms between large language models and vision models, combined with joint training strategies. This integration facilitates a paradigm shift in intelligent remote sensing interpretation—from shallow semantic matching to higher-level understanding based on world knowledge. In this study, we systematically review the research progress in the applications of MLLMs in remote sensing, specifically examining the development of Remote Sensing MLLMs (RS-MLLMs), which provides a foundation for future research directions. Initially, we discuss the concept of RS-MLLMs and review their development in chronological order. Subsequently, we provide a detailed analysis and statistical summary of the proposed architectures, training methods, applications, and corresponding benchmark datasets, along with an introduction to remote sensing agents. Finally, we summarize the research status of RS-MLLMs and discuss future research directions.

     

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