A small landslide induced a large disaster prior to the heavy rainy season in Jinkouhe, Sichuan, China: characteristics, mechanism, and lessons

Hou, Runing; Li, Zhi; Ye, Weihao; Peng, Taixin; Tian, Shufeng; Chen, Ningsheng; Huang, Na; Somos-Valenzuela, Marcelo

doi:10.1007/s10346-024-02213-z

Landslides

2024

DOI: 10.1007/s10346-024-02213-z

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A small landslide induced a large disaster prior to the heavy rainy season in Jinkouhe, Sichuan, China: characteristics, mechanism, and lessons

Runing Hou,

Zhi Li,

Weihao Ye

et al.

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Cited by 3 publications

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Insights into small landslides inducing major disasters in the Wumeng Mountain area from the Liangshui landslide

Wang,

Xing,

Liao

et al. 2024

Landslides

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Insights into small landslides inducing major disasters in the Wumeng Mountain area from the Liangshui landslide

Wang,

Xing,

Liao

et al. 2024

Landslides

View full text Add to dashboard Cite

Landslide detection based on deep learning and remote sensing imagery: A case study in Linzhi City

Wang,

Gao,

Liu

et al. 2024

Natural Hazards Research

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A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images

Zhou,

Hong,

Cui

et al. 2024

Remote Sensing

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Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing image recognition tasks because of its end-to-end pixel-level classification capability. In this study, to mitigate the disadvantages of existing landslide detection methods, we propose a multiscale attention segment network (MsASNet) that acquires different scales of remote sensing image features, designs an encoder–decoder structure to strengthen the landslide boundary, and combines the channel attention mechanism to strengthen the feature extraction capability. The MsASNet model exhibited an average accuracy of 95.13% on the test set from Bijie’s landslide dataset, a mean accuracy of 91.45% on the test set from Chongqing’s landslide dataset, and a mean accuracy of 90.17% on the test set from Tianshui‘s landslide dataset, signifying its ability to extract landslide information efficiently and accurately in real time. Our proposed model may be used in efforts toward the prevention and control of geological disasters.

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A small landslide induced a large disaster prior to the heavy rainy season in Jinkouhe, Sichuan, China: characteristics, mechanism, and lessons

Cited by 3 publications

References 44 publications

Insights into small landslides inducing major disasters in the Wumeng Mountain area from the Liangshui landslide

Insights into small landslides inducing major disasters in the Wumeng Mountain area from the Liangshui landslide

Landslide detection based on deep learning and remote sensing imagery: A case study in Linzhi City

A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images

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