Abstract
Check dams are widely used on the Loess Plateau in China to control soil and water losses,develop agricultural land,and improve watershed ecology.Detailed information on the number and spatial distribution of check dams is critical for quantitatively evaluating hydrological and ecological effects and planning the construction of new dams.Thus,this study developed a check dam detection framework for broad areas from high-resolution remote sensing images using an ensemble approach of deep learning and geospatial analysis.First,we made a sample dataset of check dams using GaoFen-2(GF-2)and Google Earth images.Next,we evaluated five popular deep-learning-based object detectors,including Faster R-CNN,You Only Look Once(version 3)(YOLOv3),Cascade R-CNN,YOLOX,and VarifocalNet(VFNet),to identify the best one for check dam detection.Finally,we analyzed the location characteristics of the check dams and used geographical constraints to optimize the detection results.Precision,recall,average precision at intersection over union(IoU)threshold of 0.50(AP50),IoU threshold of 0.75(AP75),and average value for 10 IoU thresholds ranging from 0.50-0.95 with a 0.05 step(AP50-95),and inference time were used to evaluate model performance.All the five deep learning networks could identify check dams quickly and accurately,with AP50-95,AP50,and AP75 values higher than 60.0％,90.0％,and 70.0％,respectively,except for YOLOv3.The VFNet had the best performance,followed by YOLOX.The proposed framework was tested in the Yanhe River Basin and yielded promising results,with a recall rate of 87.0％for 521 check dams.Furthermore,the geographic analysis deleted about 50％of the false detection boxes,increasing the identification accuracy of check dams from 78.6％to 87.6％.Simultaneously,this framework recognized 568 recently constructed check dams and small check dams not recorded in the known check dam survey datasets.The extraction results will support efficient watershed management and guide future studies on soil erosion in the Loess Plateau.
维普
万方数据