采用深度学习智能提取建设用地,对定量评价、监测和预测长江流域水土流失具有重要作用.本文基于开源数据,采用半自动标注方式标注建设用地,构建了面向长江流域水土流失监测的建设用地遥感数据集.该数据集在多个深度学习语义分割模型(FPN、PSPNet、DeepLabV3+、UN-et++、Swin-Transformer)测试中的总体精度(overall accu-racy,OA)均优于93.00％,均交并比(mean intersection over union,MIoU)优于70％,具有较高有效性,可推动遥感智能解译在水土流失监测中的应用.
Remote Sensing Image Dataset for Construction Land Extraction in Monitoring Soil Erosion in the Yangtze River Basin
The intelligent extraction of construction land using deep learning plays an important role in quantitative evalua-tion,monitoring and prediction of soil erosion in the Yangtze River Basin. Based on open source data,this paper adopts a semi-automatic labeling method to annotate samples,and con-structs remote sensing dataset of construction land for monitor-ing soil and water loss in the Yangtze River Basin. The over-all accuracy of multiple deep learning semantic segmentation networks (FPN,PSPNet,DeepLabV3+,UNet++,Swin-Transformer) with the testing dataset is higher than 93.00％,and MIoU is higher than 70％,which has high ef-fectiveness and can promote the application of remote sensing intelligent interpretation in soil erosion monitoring.
deep learningsoil erosion monitoringsemantic segmentationintelligent interpretation
万方数据
维普
