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Identification of Landslide in Forest Using LiDAR Data Based on Deep Learning and Persistent Homology
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Yueguang HE1, Fenghang JIANG1, Zelang MIAO2, Zhixuan BAO3, Nanzhou YI3
Mining and Metallurgical Engineering | 2023, 43(5) : 32 - 36
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Mining and Metallurgical Engineering | 2023, 43(5): 32-36
• MINING •
Identification of Landslide in Forest Using LiDAR Data Based on Deep Learning and Persistent Homology
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Yueguang HE1, Fenghang JIANG1, Zelang MIAO2, Zhixuan BAO3, Nanzhou YI3
Affiliations
  • 1.School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, Hunan, China
  • 2.School of Geosciences and info-Physics, Central South University, Changsha 410083, Hunan, China
  • 3.Hunan Water Resources and Hydropower Survey, Design, Planning and Research Co Ltd, Changsha 410119, Hunan, China
Published: 2023-10-01 doi: 10.3969/j.issn.0253-6099.2023.05.007
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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.

point cloud  /  landslide  /  digital terrain model  /  deep learning  /  persistent homology  /  extraction of landslide in forest area
Yueguang HE, Fenghang JIANG, Zelang MIAO, Zhixuan BAO, Nanzhou YI. Identification of Landslide in Forest Using LiDAR Data Based on Deep Learning and Persistent Homology[J]. Mining and Metallurgical Engineering, 2023 , 43 (5) : 32 -36 . DOI: 10.3969/j.issn.0253-6099.2023.05.007
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doi: 10.3969/j.issn.0253-6099.2023.05.007
  • Receive Date：2023-04-23
  • Online Date：2026-03-05
  • Published：2023-10-01
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  • Received：2023-04-23
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Affiliations
    1.School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410004, Hunan, China
    2.School of Geosciences and info-Physics, Central South University, Changsha 410083, Hunan, China
    3.Hunan Water Resources and Hydropower Survey, Design, Planning and Research Co Ltd, Changsha 410119, Hunan, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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