Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China

doi:10.1007/s40333-023-0091-7

Journal of Arid Land

2023, Vol. 15

Issue (1): 34-51 DOI: 10.1007/s40333-023-0091-7 CSTR: 32276.14.s40333-023-0091-7

Research article

Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China

SUN Liquan¹^,²^,³, GUO Huili¹^,², CHEN Ziyu⁴, YIN Ziming⁴, FENG Hao¹^,², WU Shufang¹^,²^,³^,^*(

), Kadambot H M SIDDIQUE⁵

¹Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China
²Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China
³College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
⁴College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
⁵The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth 6001, Australia

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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 (AP_50-95), and inference time were used to evaluate model performance. All the five deep learning networks could identify check dams quickly and accurately, with AP_50-95, 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.

Key words： check dam deep learning geospatial analysis remote sensing Faster R-CNN Loess Plateau

Received: 01 September 2022 Published: 31 January 2023

Corresponding Authors: ^*WU Shufang (E-mail: wsfjs@163.com)

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	SUN Liquan
	GUO Huili
	CHEN Ziyu
	YIN Ziming
	FENG Hao
	WU Shufang
	Kadambot H M SIDDIQUE

Cite this article:

SUN Liquan, GUO Huili, CHEN Ziyu, YIN Ziming, FENG Hao, WU Shufang, Kadambot H M SIDDIQUE. Check dam extraction from remote sensing images using deep learning and geospatial analysis: A case study in the Yanhe River Basin of the Loess Plateau, China. Journal of Arid Land, 2023, 15(1): 34-51.

URL:

http://jal.xjegi.com/10.1007/s40333-023-0091-7 OR http://jal.xjegi.com/Y2023/V15/I1/34

Fig. 1 Overview of the Yanhe River Basin and spatial distribution of partial check dams in the study area. DEM, Digital Elevation Model.

Table 1 Basic information on data used in this study

Fig. 2 Technical workflow for check dam detection across broad regions. GF-2, Gaofen-2; ASTER DEM, ASTER Global Digital Elevation Model (DEM) v3; ESA WorldCover, the European Space Agency WorldCover 10 m 2020 v100; YOLOv3, You Only Look Once (version 3); VFNet, VarifocalNet.

Fig. 3 Display of the original images (a), images with annotations (b), and augmented images (c). (a1-a4), original images of check dams; (b1-b4), images of check dams with annotation; (c1-c8), images of check dams after data augmentation.

Table 2 Hyper-parameters used for training deep learning object detection networks

Fig. 4 Check dam candidate area extraction in ArcGIS 10.2. (a), gully network extraction from ASTER Global Digital Elevation Model (DEM) v3 using the D8 algorithm; (b), establishing buffer zones around the gully network with the specified distance of 135 m; (c), check dam candidate regions extraction based on buffer zones.

Fig. 5 Comparison of precision-recall (P-R) curves for the five deep learning models at different intersection over union (IoU) thresholds. (a), P-R curves at IoU=0.50; (b), P-R curves at IoU=0.75.

Table 3 Comparison of average precision and inference speed for different object models

Fig. 6 Results of check dam detection based on VFNet and YOLOX in the Yanhe River Basin. (a), the image of new detected check dam; (b), the image of recalled check dam; (c), the image of lost check dam. Detection results in (a) and (b) show the predicted bounding box of check dam and its corresponding confidence score.

Table 4 Evaluation of check dam detections after geospatial analysis and comprehensive discrimination in the Yanhe River Basin

Fig. 7 Check dam detection results based on the integration method of deep learning and geospatial analysis in the Yanhe River Basin

Fig. 8 Spatial distribution of check dam density in the Yanhe River Basin (mapped using Kernel density with 8000 m bandwidth in ArcGIS 10.2)

Fig. 9 Results of check dam detections based on deep learning models and lost check dams. (a1-a3), correctly detected check dams; (b1-b3), incorrect detections; (c1-c3), lost check dams. Detection results in (a1-a3) and (b1-b3) show the predicted bounding box of check dam and its corresponding confidence score.

Fig. S1 Gully networks extracted by different flow accumulation cut-off values. (a), flow accumulation cut-off value=50; (b), flow accumulation cut-off value=100, (c), flow accumulation cut-off value=200; (d), flow accumulation cut-off value=300; (e), flow accumulation cut-off value=500; (f), flow accumulation cut-off value=1000.


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