Open-pit mine recognition based on Transformer model
Open-pit mine is an important object of water and soil conservation information supervision in production and con-struction projects.The efficient and accurate identification of its scope is of great significance for monitoring illegal mining behav-iors and strengthening the prevention and control of soil and water loss in the mining process.We introduced an intelligent recog-nition method utilizing a Transformer-based deep learning model for analyzing remote sensing images of open-pit mining areas.Comparative experiments were conducted on the open-pit mine dataset in Yibin City,Sichuan Province,using widely adopted deep learning recognition methods based on convolutional neural networks.The results indicated that the reveal precision,recall,F1-score,and IoU values of this method for identifying the scope of open-pit mines were 91.25%,90.66%,90.95%and 83.41%,respectively,which can meet the accuracy requirements of remote sensing supervision for water and soil conservation.Additionally,the efficiency and accuracy of our method remained superior to the contrasted methods while it shows equivalent run-ning efficiency,indicating significant practical utility.The method introduced in this paper holds substantial potential for wide-spread application,enabling swift and accurate recognition of open-pit mines across extensive regions.
water and soil conservationremote sensing supervisionopen-pit minedeep learningTransformer modelse-mantic segmentationYibin City
国家科技期刊平台
NSTL
万方数据
