Landslide hazard identification based on the object detection algorithm YOLOv9:taking Yongxin county as an example

doi:10.13474/j.cnki.11-2246.2025.0607

Abstract

Abstract: Landslide disaster is one of the most serious geological disasters,which causes huge property losses and casualties every year.Traditional image-based manual investigation is heavy in workload and low in efficiency.This study takes Yongxin county as the research area,firstly,uses the YOLOv9 object detection algorithm to build a landslide recognition model based on 207 landslide samples constructed by high-resolution aerial images,and then evaluates the accuracy of the model.Finally,the landslide of the whole county is identified and the landslide results identified are analyzed.The results show that the accuracy of the model is 0.98,the recall rate is 0.97,and the mAP is 0.95.There are 312 common landslides in the county,and 46 are misjudged by the model through comparison and field investigation,and the accuracy of model recognition is 85.26%.It can be seen that YOLOv9,an object detection algorithm,can effectively identify landslides in the southern region,providing an effective solution for large-scale identification of small-scale landslides in the south.

Key words: landslide, target detection, remote sensing, YOLOv9, automatic recognition

CLC Number:

P237

TU Liping, CHEN Meiqiu, LENG Peng. Landslide hazard identification based on the object detection algorithm YOLOv9:taking Yongxin county as an example[J]. Bulletin of Surveying and Mapping, 2025, 0(6): 37-42,102.

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