Landslide recognition based on convolutional neural network

Zilin Ding; Xinqiang Yao; Luqiang Sun; Yajing Li

doi:10.1117/12.2668101

23 February 2023 Landslide recognition based on convolutional neural network

Zilin Ding, Xinqiang Yao, Luqiang Sun, Yajing Li

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125511H (2023) https://doi.org/10.1117/12.2668101
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

It is important to access the disaster distribution for earthquake disaster assessment and emergency command. Quick access to post-seismic disaster information is necessary for emergency command. Based on the post-earthquake landslide image dataset, a residual network model is used to identify disaster information on landslide image data using migration learning techniques. The research results show that the use of deep learning methods can better analyse landslide images, with recognition accuracy reaching over 93%, and can effectively extract disaster information from the images, providing technical support for the automatic analysis of emergency disaster data.

Citation Download Citation

Zilin Ding, Xinqiang Yao, Luqiang Sun, and Yajing Li "Landslide recognition based on convolutional neural network", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125511H (23 February 2023); https://doi.org/10.1117/12.2668101

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