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In order to obtain the information of landslide in forest area, a high-resolution digital terrain model was derived from LiDAR point cloud, and the data of landslide in forest area were extracted by Res-Unet network and persistent homology. The Fenghe Experimental Forest in Washington State of USA was selected for study, among which three areas were selected for quantitative analysis. Based on calculation, the extracted data of landslide in the forest area show an average precision of 79.7%, an average recall rate of 70.2%, and average F₁ of 65.5%. The extraction method based on Res-Unet and persistent homology can accurately identify most landslides in the research area. It is shown that by using deep learning and persistent homology, this extraction method can make up for the weakness of traditional remote sensing methods in extracting landslide information in vegetation covered areas, and also provide a technical support for landslide analysis.

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为了从森林覆盖区获取林下滑坡信息，采用LiDAR点云技术构建高分辨率数字地形模型，结合Res-Unet网络和持续同调理论提取林下滑坡信息。选取美国华盛顿州风河实验林作为研究区，选择其中3个区域进行定量分析，经计算，区域内提取滑坡信息的准确度均值为79.7%，召回率均值为70.2%，F₁均值为65.5%，表明基于Res-Unet和持续同调的提取方法能够准确识别研究区内大部分滑坡；基于深度学习和持续同调的林下滑坡提取方法引入持续同调方法到滑坡提取领域，并与深度学习相结合，弥补了传统遥感方法在植被覆盖区滑坡提取效果方面的不足，可为滑坡分析提供有力的技术支持。

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贺跃光（1966—），男，湖南益阳人，博士，教授，主要研究方向为工程灾害监测与治理。

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贺跃光（1966—），男，湖南益阳人，博士，教授，主要研究方向为工程灾害监测与治理。

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