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Research on Automatic Landslide Identification Based on Faster R-CNN and Mask R-CNN
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Xianyu YU1, Sen YANG1
Journal of Geodesy and Geodynamics | 2025, 45(1) : 1 - 4, 12
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Journal of Geodesy and Geodynamics | 2025, 45(1): 1-4, 12
Research on Automatic Landslide Identification Based on Faster R-CNN and Mask R-CNN
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Xianyu YU1, Sen YANG1
Affiliations
  • 1 School of Civil Engineering Architecture and Environment, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, China
  • YU Xianyu, PhD, assistant professor, majors in evaluation of landslide susceptibility, E-mail:

About Author:

YU Xianyu, PhD, assistant professor, majors in evaluation of landslide susceptibility, E-mail:

Published: 2025-01-15 doi: 10.14075/j.jgg.2024.03.090
Outline
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Based on the images from Gaofen-1 satellite, taking the first section of the Three Gorges reservoir as an example, 160 landslide samples are visually interpreted and divided into training and validation samples according to the ratio of 9:1. Then, landslide automatic identification models are constructed using Mask R-CNN and Faster R-CNN algorithms. To further compare and analyze the performance of the two models under different sample ratios, calculations are performed using sample ratios of 8:2, 7:3, and 6:4. The results show that the recognition results based on Mask R-CNN model are better than Faster R-CNN in three indicators, including precision, recall and F1 score. Moreover, cross-validation proves that the performance of Mask R-CNN model is more stable.

deep learning  /  landslide identification  /  Mask R-CNN  /  Faster R-CNN  /  cross-validation
于宪煜, 杨森. 基于Faster R-CNN和Mask R-CNN的滑坡自动识别研究. 大地测量与地球动力学, 2025 , 45 (1) : 1 -4, 12 . DOI: 10.14075/j.jgg.2024.03.090
Xianyu YU, Sen YANG. Research on Automatic Landslide Identification Based on Faster R-CNN and Mask R-CNN[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (1) : 1 -4, 12 . DOI: 10.14075/j.jgg.2024.03.090
  • National Natural Science Foundation of China(41807297)
  • Ecological Environment Geotechnical and River and Lake Ecological Restoration Discipline Intelligence Introduction and Innovation Demonstration Base Fund(2020EJB004)
Year 2025 volume 45 Issue 1
26
10
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Article Info
doi: 10.14075/j.jgg.2024.03.090
  • Receive Date:2024-03-05
  • Online Date:2026-10-30
  • Published:2025-01-15
Article Data
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History
  • Received:2024-03-05
Funding
National Natural Science Foundation of China(41807297)
Ecological Environment Geotechnical and River and Lake Ecological Restoration Discipline Intelligence Introduction and Innovation Demonstration Base Fund(2020EJB004)
Affiliations
    1 School of Civil Engineering Architecture and Environment, Hubei University of Technology, 28 Nanli Road, Wuhan 430068, 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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