电力勘测设计Issue(z1)：16-23,8.DOI:10.13500/j.dlkcsj.issn1671-9913.2025.S1.003
基于深度学习的滑坡识别
Landslide Identification Based on Deep Learning——From Remote Sensing Images to Practical Applications
摘要
Abstract
In the actual engineering process,landslide identification relies on manual interpretation,has problems such as low efficiency and strong subjectivity.To solve these problems,takes the areas along typical power line projects as the application scenarios and adopts Deep Learning algorithms to identify the landslide areas.This method not only improves the accuracy of landslide identification but also verifies its feasibility in engineering applications.This paper constructs a landslide dataset by integrating open-source images and high-resolution aerial survey data.In this paper,multi-level features are extracted by using the image encoder of the improved SAM,and then high-precision segmentation results are generated through the cross-feature fusion decoder.The research results show that the method adopted in this study performs well in terms of the identification accuracy of landslide boundaries,with the identification accuracy reaching 90.31%.Deep Learning methods reduce manual intervention and can provide reliable technical support for the identification of geological disasters in power line engineering.关键词
线路工程/深度学习/滑坡识别/特征融合Key words
power line projects/deep learning/landslide identification/feature fusion分类
能源科技引用本文复制引用
任晖,库新勃,杨帅,张海龙..基于深度学习的滑坡识别[J].电力勘测设计,2025,(z1):16-23,8.基金项目
中国电力工程顾问集团有限公司集中开发科研项目"基于人工智能的电力工程勘测数据辅助应用关键技术研究"(DG2-L03-2024). （DG2-L03-2024）
