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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (1): 34-51    DOI: 10.1007/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 (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.

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.

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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.
Data type Product name Data time Spatial resolution (m) Source
Remote sensing image GaoFen-2 2020 PMS: 4.0; PAN: 1.0 Land Observation Satellite Data Service Platform of the China Resources Satellite Application Center
Google Earth image 2020 0.3 and 1.0 Google Earth
DEM ASTER Global DEM v3 2019 30.0 National Aeronautics and Space Administration Earthdata
Land cover ESA WorldCover 10 m 2020 v100 2020 10.0 ESA
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.
Model Faster R-CNN Cascade R-CNN YOLOv3 YOLOX VFNet
Backbone ResNeXt-101 ResNeXt-101 Darknet-53 CSPDarknet Res2Net-101
Batch 2 2 12 4 2
Optimizer SGD SGD SGD SGD SGD
Momentum 0.90 0.90 0.90 0.90 0.90
Weight decay 0.0001 0.0001 0.0005 0.0005 0.0001
Base learning rate 0.00250 0.00250 0.00100 0.00125 0.00125
Epoch 24 24 30 30 24
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.
Model Backbone Average precision (%) Inference speed (image/s)
IoU=0.50:0.95 IoU=0.50 IoU=0.75
Faster R-CNN ResNeXt-101 61.5 92.5 72.3 4.2
Cascade R-CNN ResNeXt-101 67.0 94.9 78.5 3.9
YOLOv3 Darknet-53 58.1 94.3 66.5 25.4
YOLOX CSPDarknet 69.4 96.4 80.3 15.8
VFNet Res2Net-101 69.9 97.2 82.5 5.5
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.
Method Precision evaluation Recall evaluation
Detection box Correct
detection		 Precision (%) Validation data
of check dams		 Recalled check
dams		 Recall (%)
DL 1390 1092 78.6 602 524 87.0
DL+GA 1243 1089 87.6 521 86.5
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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