Abstract
In the current era, remote sensing is a powerful platform for detecting and predicting landslides. Moreover, the advancement in computing technologies has proven significant in artificial intelligence (AI) research. Researchers have made significant attempts in the existing literature by introducing landslide detection procedures from remote sensing images (RSIs) through deep learning (DpLr) algorithms. This research work aims to survey those methods. Our database consists of 204 published research articles. In addition, 50% (approximately) of the papers are directly related to landslide information extraction from satellite and unmanned aerial vehicles (UAV) images exploiting DpLr models. The suggested methods have been categorized into seven parts based on the applied model. Further, the evaluation methods have been discussed. The quantitative results are based on the following parameters: (1) contributing nations, (2) key study locations, (3) data set distribution, and (4) model utilization. Lastly, challenges in the studies of DpLr algorithms and the opportunities in landslide detection problems are discussed to motivate future research.
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Acknowledgements
The authors would like to thank the director of the Wadia Institute of Himalayan Geology (WIHG) for his continuous motivation and encouragement. The WIHG contribution number for this research work is WIHG/0236. This work is supported by the Department of Science and Technology, Science and Engineering Research Board, New Delhi, India, under Grant No: EEQ/2022/000812.
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Chandra, N., Vaidya, H. Deep learning approaches for landslide information recognition: Current scenario and opportunities. J Earth Syst Sci 133, 85 (2024). https://doi.org/10.1007/s12040-024-02281-8
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DOI: https://doi.org/10.1007/s12040-024-02281-8