面向长江流域水土流失监测中建设用地提取的遥感影像数据集  

Remote Sensing Image Dataset for Construction Land Extraction in Monitoring Soil Erosion in the Yangtze River Basin

作　　者：郭娇 张鹏飞 李畅 GUO Jiao;ZHANG Pengfei;LI Chang(Key Laboratory for Geographical Process Analysis and Simulation,Wuhan 430079,China;College of Urban and Environmental Science,Central China Normal University,Wuhan 430079,China)

机构地区：[1]湖北省地理过程分析与模拟重点实验室,湖北武汉430079 [2]华中师范大学城市与环境科学学院,湖北武汉430079

出　　处：《测绘地理信息》2024年第3期96-100,共5页Journal of Geomatics

基　　金：国家自然科学基金(41771493,41101407);中央高校基本科研业务费资助(CCNU22QN019)。

摘　　要：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。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.

关 键 词：深度学习 水土流失监测 语义分割 智能解译 

分 类 号：P237[天文地球—摄影测量与遥感]