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The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Author

Listed:
  • Zhujun Gu

    (Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China
    These authors contributed equally to this work.)

  • Maimai Zeng

    (Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China
    These authors contributed equally to this work.)

Abstract

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

Suggested Citation

  • Zhujun Gu & Maimai Zeng, 2023. "The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives," Sustainability, MDPI, vol. 16(1), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:274-:d:1308878
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    References listed on IDEAS

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    1. Yongguang Hu & Ali Raza & Neyha Rubab Syed & Siham Acharki & Ram L. Ray & Sajjad Hussain & Hossein Dehghanisanij & Muhammad Zubair & Ahmed Elbeltagi, 2023. "Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
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