Intelligent identification of landslide region based on MobileNet network

MA Jianliang; ZHANG Zishan; LI Jianguang; LIU Xin; JIE Yuxin

doi:10.20265/j.cnki.issn.1007-2993.2024-0295

Volume 40 Issue 1

Feb. 2026

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MA Jianliang, ZHANG Zishan, LI Jianguang, LIU Xin, JIE Yuxin. Intelligent identification of landslide region based on MobileNet network[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2026, 40(1): 18-25. doi: 10.20265/j.cnki.issn.1007-2993.2024-0295

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MA Jianliang, ZHANG Zishan, LI Jianguang, LIU Xin, JIE Yuxin. Intelligent identification of landslide region based on MobileNet network[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2026, 40(1): 18-25. doi: 10.20265/j.cnki.issn.1007-2993.2024-0295

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Intelligent identification of landslide region based on MobileNet network

doi: 10.20265/j.cnki.issn.1007-2993.2024-0295

MA Jianliang^1
,,
ZHANG Zishan^{1, 2, 3
,
,},
LI Jianguang¹,
LIU Xin¹,
JIE Yuxin^{2, 3}

1.
AVIC institute of Geotechnical Engineering Co., Ltd., Beijing 100098, China
2.
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
3.
Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China

Received Date: 2024-06-27
Accepted Date: 2025-08-25
Rev Recd Date: 2025-08-14
Publish Date: 2026-02-06

Abstract

Abstract

Conventional landslide cataloging and statistical analysis predominantly rely on manual field surveys, which are characterized by inefficiency and the potential for omitting certain areas. Currently, mainstream slope cataloging techniques based on image recognition typically necessitate high-performance equipment and incur substantial model training costs, thereby hindering their efficient application in on-site rapid screening scenarios. This study incorporates the MobileNet lightweight model and leverages the DeepLabV3 architecture to realize rapid intelligent identification and boundary localization of landslides in aerial photographic imagery. In comparison with traditional convolutional neural network (CNN) image segmentation approaches, the proposed method can achieve an accuracy exceeding 90% within 10% of the training duration required by conventional schemes. This renders it more adept at satisfying engineering demands for the rapid intelligent recognition of landslides and makes it suitable for the rapid screening and cataloging of landslide points across large-scale regions.
- landslides,
- intelligent recognition,
- deep learning,
- MobileNet network

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References(14)

References

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Intelligent identification of landslide region based on MobileNet network

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