Application of Unmanned Aerial Vehicle Remote Sensing for Geological Disaster Reconnaissance along Transportation Lines: A Case Study

2012 ◽  
Vol 226-228 ◽  
pp. 2376-2379 ◽  
Author(s):  
Ji Ping Hu ◽  
Wen Bin Wu ◽  
Qu Lin Tan
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
High Efficiency ◽  
Scientific Data ◽  
Geological Investigation ◽  
Geological Disaster ◽  
Transportation Line ◽  
Aerial Vehicle ◽  
Engineering Geological Investigation

Compared with conventional airborne remote sensing application to engineering geological investigation, High precision Unmanned Aerial Vehicle Remote Sensing (UAV-RS) technology can improve work condition with advantages of high flexibility, low cost, high efficiency and up-to-date situation acquisition. Especially, it has very important engineering significance for quick and urgent geological disaster reconnaissance along transportation lines. In the paper, some aspects of application to transportation-line (pipeline, highway and railway) engineering geological investigation were discussed. The concerned key points, including components of UAV-RS system, data processing workflow and image interpretation were analyzed. As a case study, a UAV-RS application project for transportation-line geological disaster investigation was given. The utilization of this new remote sensing technology successfully collected and discovered potential geological disasters and provided scientific data for timely decision-making.

scholarly journals Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

2021 ◽  
Vol 13 (18) ◽  
pp. 3652
Author(s):  
Duo Xu ◽  
Yixin Zhao ◽  
Yaodong Jiang ◽  
Cun Zhang ◽  
Bo Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
High Efficiency ◽  
Detection Method ◽  
Laplacian Operator ◽  
Ground Fissures ◽  
Aerial Vehicle ◽  
Edge Detection Method

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 a.m. and 5:00 a.m. under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 a.m., 1:00 p.m. and 3:00 p.m.

scholarly journals Application of Unmanned Aerial Vehicle Remote Sensing in Engineering Measurement

2020 ◽  
Vol 9 (2) ◽  
pp. 42
Author(s):  
Qing Li
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
High Efficiency ◽  
High Accuracy ◽  
Simple Operation ◽  
Engineering Measurement ◽  
Mapping Technology ◽  
Aerial Vehicle ◽  
Surveying And Mapping

<p>Unmanned aerial vehicle remote sensing is widely used in the whole engineering measurement in recent years. It has many advantages including simple operation, high accuracy and high efficiency. It is precisely because of these advantages that unmanned aerial vehicle remote sensing has gradually replaced the traditional surveying and mapping technology to be widely used. With the continuous expansion of the number and scale of projects in China, the effect of unmanned aerial vehicle remote sensing in engineering measurement is getting bigger and bigger. This article mainly analyzes the advantages of unmanned aerial vehicle remote sensing and its application in engineering measurement, so as to provide some reference for the development of surveying and mapping engineering in China.</p>

Determining Shoreline Response to Meteo-oceanographic Events Using Remote Sensing and Unmanned Aerial Vehicle (UAV): Case Study in Southern Brazil

2018 ◽  
Vol 85 ◽  
pp. 766-770 ◽  
Author(s):  
Miguel da G. Albuquerque ◽  
Deivid C. Leal Alves ◽  
Jean M. de A. Espinoza ◽  
Ulisses R. Oliveira ◽  
Rodrigo S. Simões
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Southern Brazil ◽  
Aerial Vehicle

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

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