Extensive identification of landslide boundaries using remote sensing images and deep learning method

Abstract

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue. It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response. Therefore, the Skip Connection DeepLab neural network (SCDnn), a deep learning model based on 770 optical remote sensing images of landslide, is proposed to improve the accuracy of landslide boundary detection. The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features. SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block (ASPC) with a coding structure that reduces model complexity. The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8 and 0.9; while 52 images with MIoU values exceeding 0.9, which exceeds the identification accuracy of existing techniques. This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future investigations and applications in related domains.

Keywords

Geohazard / Landslide boundary detection / Remote sensing image / Deep learning model / Steep slope / Large annual rainfall / Human settlements / Infrastructure / Agricultural land / Eastern Tibetan Plateau / Geological hazards survey engineering

CRediT authorship contribution statement

Chang-dong Li, Peng-fei Feng, Shuang Zhang and Jie Meng conceived of the presented idea. Peng-fei Feng, Shuang Zhang, Xi-hui Jiang and Jie Meng carried out the experiment. All authors discussed the results and contributed to the final manuscript.

Declaration of competing interest

The authors declare no conflicts of interest.

Acknowledgment

The research is supported by the National Natural Science Foundation of China (Grant Nos. 42090054, 41931295), the Natural Science Foundation of Hubei Province of China (2022CFA002).

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