Ephemeral gully recognition and accuracy evaluation using deep learning in the hilly and gully region of the Loess Plateau in China

作     者：Boyang Liu Biao Zhang Hao Feng Shufang Wu Jiangtao Yang Yufeng Zou Kadambot H.M.Siddique Boyang Liu;Biao Zhang;Hao Feng;Shufang Wu;Jiangtao Yang;Yufeng Zou;Kadambot H.M.Siddique

作者机构：Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid AreasMinistry of EducationNorthwest A&F UniversityYangling712100China Institute of Water Saving Agriculture in Arid Areas of ChinaNorthwest A&F UniversityYangling712100China College of Water Resources and Architectural EngineeringNorthwest A&F UniversityYangling712100China Department of Foreign LanguagesNorthwest A&F UniversityYangling712100China The UWA Institute of Agriculture and School of Agriculture&EnvironmentThe University of Western AustraliaPerthWA6001Australia 

出 版 物：《International Soil and Water Conservation Research》 (国际水土保持研究（英文）)

年 卷 期：2022年第10卷第3期

页      面：371-381页

核心收录：

学科分类：082802[工学-农业水土工程] 090707[农学-水土保持与荒漠化防治] 0907[农学-林学] 08[工学] 0828[工学-农业工程] 09[农学] 0815[工学-水利工程] 

基　　金：This research was supported by the National Natural Science Foundation of China(41977064) the Fundamental Research Funds for the Central Universities(2452021158 2452021036) the 111 Project of the Ministry of Education and the State Administration of Foreign Experts Affairs(B12007) 

主　　题：Deep learning Remote sensing image Ephemeral gully recognition Loess plateau Image semantic segmentation Accuracy evaluation 

摘      要：Ephemeral gullies are widely distributed in the hilly and gully region of the Loess Plateau and play a unique role in the slope gully erosion *** and accurate identification of ephemeral gullies impacts the distribution law and development trend of soil erosion on the Loess *** learning algorithms can quickly and accurately process large data samples that recognize ephemeral gullies from remote sensing ***,we investigated ephemeral gullies in the Zhoutungou watershed in the hilly and gully region of the Loess Plateau in China using satellite and unmanned aerial vehicle images and combined a deep learning image semantic segmentation model to realize automatic recognition and feature *** Accuracy,Precision,Recall,F1value,and AUC,we compared the ephemeral gully recognition results and accuracy evaluation of U-Net,R2U-Net,and SegNet image semantic segmentation *** SegNet model was ranked first,followed by the R2U-Net and U-Net models,for ephemeral gully recognition in the hilly and gully region of the Loess *** ephemeral gully length and width between predicted and measured values had RMSE values of 6.78 m and 0.50 m,respectively,indicating that the model has an excellent recognition *** study identified a fast and accurate method for ephemeral gully recognition in the hilly and gully region of the Loess Plateau based on remote sensing images to provide an academic reference and practical guidance for soil erosion monitoring and slope and gully management in the Loess Plateau region.