Abstract
Loess landslides are one of the geological hazards prevalent in mountainous areas of Loess Plateau, seriously threatening people’s lives and property safety. Accurate identification of landslides is a prerequisite for reducing the risk of landslide hazards. Traditional landslide interpretation methods often have the disadvantage of being laborious and difficult to use on a large scale compared with the recently developed deep learning-based landslide detection methods. In this study, we propose an improved deep learning model, landslide detection- you only look once (LD-YOLO), based on the existing you only look once (YOLO) model for the intelligent identification of old and new landslides in loess areas. Specifically, remote sensing images of landslides in Baoji City, Shaanxi Province, China are acquired from the Google Earth Engine platform. The landslide images of Baoji City (excluding Qianyang County) are used to establish a loess landslide dataset for training the model. The landslide data of Qianyang County is used to verify the detection performance of the model. The focal and efficient IoU (Focal-EIoU) loss function and efficient channel attention (ECA) mechanism are incorporated into the 7th version of YOLO (YOLOv7) model to construct the LD-YOLO model, which makes it more suitable for the landslide detection task. The experiments yielded an improved LD-YOLO model with average precision of 92.05%, precision of 92.31%, recall of 90.28%, and F1-score of 91.28% for loess landslide detection. The landslides in Qianyang County were divided into two test sets, new landslides and old landslides, which were used to test the detection performance of LD-YOLO for both types of landslides. The results show that LD-YOLO detects old landslides with a detection precision of 82.75% and a recall of 80%. When detecting new landslides, the detection precision is 94.29% and the recall is 91.67%. It indicates that our proposed LD-YOLO model has strong detection performance for both new and old landslides in loess areas. Through a proposed solution that can realize the accurate detection of landslides in loess areas, this paper provides a valuable reference for the application of deep learning methods in landslide identification.
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Availability of Data/Materials: The location information, topography information, and remote sensing images involved in this study were obtained from the Google Earth Engine (GEE) and Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn/DataList.aspx).
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Acknowledgments
The paper was supported by the Huainan Normal University Natural Science Research (Grants No. 2022XJYB034), the Fundamental Research Funds for the Central Universities, CHD (Grants No. 300102352506), and the Natural Science Foundation of Anhui Colleges (Grants No. KJ2020A0313).
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Material preparation, data collection and analysis were performed by LIU Qing, WU Ting-ting, DENG Ya-hong, LIU Zhi-heng. The first draft of the manuscript was written by LIU Qing and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Liu, Q., Wu, Tt., Deng, Yh. et al. Intelligent identification of landslides in loess areas based on the improved YOLO algorithm: a case study of loess landslides in Baoji City. J. Mt. Sci. 20, 3343–3359 (2023). https://doi.org/10.1007/s11629-023-8128-0
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DOI: https://doi.org/10.1007/s11629-023-8128-0