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Sematic segmentation of loess landslides with STAPLE mask and fully connected conditional random field

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Abstract

Loess landslides are widely distributed in Northern China and pose a significant threat to human life, natural resources, and infrastructure in mountainous regions. Accurate segmentation and measurement of loess landslides is crucial to documenting their occurrence and extent and investigating the distribution, types, and patterns of slope failures. The measurement of landslides also assists in assessing their susceptibility and risk. Herein, a novel loess landslide segmentation and measurement framework based on deep learning is proposed. Multiple experts label the ground-truth landslide regions, and simultaneous truth and performance level estimation (STAPLE) masks are generated. The U-Net segmentation algorithm is trained using a supervised approach to segment the loess landslide region. The fully connected conditional random field is integrated into the U-Net to further optimize the segmentation quality. In the final step, the predicted landslide boundaries are visualized, and the diameters (e.g., length and width) of the segmentation outcome are computed simultaneously. Four state-of-the-art segmentation algorithms are selected for the comparative analysis. The computational results demonstrate that the proposed framework outperforms all the other algorithms tested in terms of segmentation accuracy and boundary errors. The results verify the advantages of using STAPLE and U-Net integrated with a conditional random field.

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Funding

This research is supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 41521002), the Major Program of the National Natural Science Foundation of China (Grant No. 41790445), the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (Grant No. SKLGP2021K014), the “Miaozi project” of scientific and technological innovation in Sichuan Province, China (Grant No. 2021090), the key research program of Sichuan Province, China (Grant: No. 22ZDYF2365), and the Project of remote sensing identification and monitoring of geological hazards in Sichuan province, CN (2020) (Grant No. 510201202076888).

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Author notes

  1. Huajin Li and Yusen He contributed equally to this work.

Authors and Affiliations

  1. School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, Sichuan, China

    Huajin Li & Yusen He

  2. State Key Laboratory of Geo-hazard Prevention and Geo-Environment Protection, Chengdu University of Technology, No. 1 Erxianqiao East Rd., Chengdu, Sichuan, 610059, China

    Huajin Li, Qiang Xu & Weile Li

  3. Data Analysis and Social Inquiry Lab (DASIL), Grinnell College, Grinnell, IA, 50112, USA

    Yusen He

  4. College of Computing and Digital Media, DePaul University, Chicago, IL, 60604, USA

    Jiahao Deng

  5. Architectural Engineering College, Guizhou Minzu University, Guiyang, 550025, Guizhou, China

    Yong Wei

  6. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, Sichuan, China

    Jingren Zhou

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  1. Huajin Li
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Correspondence to Qiang Xu.

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Li, H., He, Y., Xu, Q. et al. Sematic segmentation of loess landslides with STAPLE mask and fully connected conditional random field. Landslides 20, 367–380 (2023). https://doi.org/10.1007/s10346-022-01983-8

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