山东国土资源2026，Vol.42Issue(6)：54-61,8.DOI:10.12128/j.issn.1672-6979.2026.06.007
BiSeNet深度学习模型在四川阿坝州地区滑坡识别中的应用
Application of the BiSeNet Deep Learning Model in Landslide Detection in Aba Prefecture in Sichuan Province
马亮亮 1史健 1高云雷 1荆欣 1满雪婷 1田庆安1
作者信息
- 1. 山东省地质矿产勘查开发局第二水文地质工程地质大队(山东省鲁北地质工程勘察院),山东 德州 253072||高分辨率对地观测系统山东德州数据与应用中心,山东 德州 253072
- 折叠
摘要
Abstract
Landslide has brought huge losses to human production and life.Its identification is the basis of landslide susceptibility,hazard and risk assessment,and provides data support for disaster management.Traditional visual interpretation requires expert experience,time-consuming and labor-intensive,while automatic classification method has low accuracy and cannot be applied on a large scale.Accompanying with the development of artificial intelligence,automatic landslide identification using high-resolution im-ages has become a hot research topic.In existing automatic recognition,the landslide is mainly composed of machine learning,with the application of the image as a data source less Google earth.In this paper,taking aba Tibetan and qiang autonomous prefecture in Sichuan province as the research area,using topog-raphy factor Google earth images as data sources,based on small samples,by using BiSeNet depth study method,landslide has been identified.First,the landslide sample set has been constructed based on Google Earth,and the landslide sample has been segmented and labeled by Labelme annotation tool.Sec-ondly,BiSeNet model has been used for semantic segmentation to train the landslide of sample set and la-bel set.Finally,the training model has been used to complete automatic landslide identification verification on the landslide data of the verification set.Combining with actual field verification,the accuracy of small sample identification method is 85.4%.It is showed that BiSeNet model based on image segmentation is effective for landslide recognition of small sample.关键词
滑坡/深度学习/自动识别/语义分割/BiSeNet/四川阿坝州地区Key words
Landslide/deep learning/automatic identification/semantic segmentation/BiSeNet/Aba Pre-fecture in Sichuan province分类
天文与地球科学引用本文复制引用
马亮亮,史健,高云雷,荆欣,满雪婷,田庆安..BiSeNet深度学习模型在四川阿坝州地区滑坡识别中的应用[J].山东国土资源,2026,42(6):54-61,8.
