The Innovation Geoscience > 2024 Vol. 2 > No. 1 > 100055
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Multimodal artificial intelligence foundation models: Unleashing the power of remote sensing big data in earth observation

  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    School of Mathematics and Statistics, Southeast University, Nanjing 211189, China

  • 4.

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • 5.

    Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277–8561, Japan

  • 6.

    Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik 102, Iceland

  • 7.

    University Grenoble Alpes, Grenoble 38000, France

  • 8.

    These authors contributed equally

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  • Corresponding author: zb@radi.ac.cn 
  • Received Date:  30 January 2024
    Accepted Date:  06 March 2024
    Published Online:  14 March 2024
The Innovation Geoscience  2(1),  https://doi.org/10.59717/j.xinn-geo.2024.100055  |  Cite this article
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    • REFERENCES
  • [1] Dalla Mura, M., Prasad, S., Pacifici, F., et al. (2015). Challenges and opportunities of multimodality and data fusion in remote sensing. Proceedings of the IEEE, 103(9): 1585−1601. DOI: 10.1109/JPROC.2015.2462751.

    View in Article CrossRef Google Scholar

    [2] Hong, D., Zhang, B., Li, H., et al. (2023). Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks. Remote Sensing of Environment, 299: 113856. DOI:10.1016/j.rse.2023.113856.

    View in Article Google Scholar

    [3] Xu, Y., Liu, X., Cao, X., et al. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2(4): 100179. DOI: 10.1016/j.xinn.2021.100179.

    View in Article CrossRef Google Scholar

    [4] Zhang, B., Wu, Y., Zhao, B., et al. (2022). Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 1814−1822. DOI: 10.1109/JSTARS.2022.3148139.

    View in Article CrossRef Google Scholar

    [5] Hong, D., Zhang, B., Li, X., et al. (2024). SpectralGPT: Spectral remote sensing foundation model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024. DOI: 10.1109/TPAMI.2024.3362475.

    View in Article Google Scholar

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  • Cite this article:

    Hong D., Li C., Zhang B., et al., (2024). Multimodal artificial intelligence foundation models: Unleashing the power of remote sensing big data in earth observation. The Innovation Geoscience 2(1): 100055. https://doi.org/10.59717/j.xinn-geo.2024.100055
    Hong D., Li C., Zhang B., et al., (2024). Multimodal artificial intelligence foundation models: Unleashing the power of remote sensing big data in earth observation. The Innovation Geoscience 2(1): 100055. https://doi.org/10.59717/j.xinn-geo.2024.100055

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