Research on Landslide Detection in High-Resolution Remote Sensing Image Based on Deep Learning
CHEN Long
GE Cheng
DAI Yingchao
WANG Hongyu
LIU Weiwei
Abstract：In response to the current issue of low precision in landslide detection,a deep learning-based landslide detection framework was proposed.This framework includeed three parts:data collection and processing,feature selection,and detection model,which can effectively improve the detection capability of landslides by integrating multi-source data.A multimodal KlAlexNet model was proposed,which could achieve pixel-level segmentation pre-diction and effectively fuse spatial features.Experimental results indicate that the proposed KlAlexNet model has high precision in landslide detection.Compared with methods such as U-Net,U-Net++,FC_DenseNet,and YO-LOv9-seg,it demonstrates advantages.The experimental results validate the effectiveness and practicality of the proposed method,indicating its broad application prospects.
Keywords：Deep learningLandslide detectionConvolutional neural networksFeature extractionLoss func-tion
Publication Date：2025-06-30
Online Publishing Date：2025-08-25(First online date of this platform, not the publication date of the document)
Pages：9( 66-74 )