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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 1-6     DOI: 10.6046/gtzyyg.2020.01.01
Tethered UAVs-based applications in emergency surveying and mapping
Yongquan WANG1,2, Qingquan LI1, Chisheng WANG1,2,3(), Jiasong ZHU1,2, Xinyu WANG1,2
1. Guangdong Key Laboratory of Urban Informatics, School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
2. Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China
3. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural and Resources, Shenzhen 518000, China
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Traditional surveying and mapping pay much attention to “accuracy”, but ignore the “speed”. Modern emergency mapping needs to be “speedy and accurate”. If traditional mapping techniques are used, it will consume a lot of time, which will affect the timeliness of emergency response. A tethered UAVs can collect high-quality data and realize real-time long-term video monitoring. This paper introduces the application status, characteristic advantages, and application scenarios of the tethered UAVs. And a kind of experience and method for using tethered UAVs to collect high quality image data and video data for producing surveying and mapping data products and identifying video targets through Darknet deep learning framework is proposed. Based on many simulation experiments and practical applications, the authors hold that this method is effective in providing timely and effective surveying and mapping guarantee for disaster relief and rescue.

Keywords tethered UAVs      emergency surveying and mapping      Agisoft PhotoScan      Darknet     
:  TP79  
Corresponding Authors: Chisheng WANG     E-mail:
Issue Date: 14 March 2020
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Yongquan WANG
Qingquan LI
Chisheng WANG
Jiasong ZHU
Xinyu WANG
Cite this article:   
Yongquan WANG,Qingquan LI,Chisheng WANG, et al. Tethered UAVs-based applications in emergency surveying and mapping[J]. Remote Sensing for Land & Resources, 2020, 32(1): 1-6.
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算法 训练集 测试集 平均精
FLOPS FPS Cfg Weights
SSD300 COCO trainval test-dev 41.2 - 46 link
Tiny YOLO COCO trainval test-dev 23.7 5.41 Bn 244 cfg weights
DSSD321 COCO trainval test-dev 46.1 - 12 link
R-FCN COCO trainval test-dev 51.9 - 12 link
SSD513 COCO trainval test-dev 50.4 - 8 link
DSSD513 COCO trainval test-dev 53.3 - 6 link
FPN FRCN COCO trainval test-dev 59.1 - 6 link
Retinanet-101-500 COCO trainval test-dev 53.1 - 11 link
YOLOv3-320 COCO trainval test-dev 51.5 38.97 Bn 45 cfg weights
YOLOv3-tiny COCO trainval test-dev 33.1 5.56 Bn 220 cfg weights
YOLOv3-spp COCO trainval test-dev 60.6 141.45 Bn 20 cfg weights
Tab.1  Comparison of YOLO and other algorithms for processing COCO data sets
Fig.1  DOM and real-life 3D model of the collapsed area of the Guangming New District, Shenzhen
Fig.2  Tethered UAVs system
Fig.3  Crowd identification in Shenzhen University military training in 2019
Fig.4  Recognition in the traffic accident scene of Changzhou section of Shanghai-Nanjing Expressway in 2016
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