Accuracy of Drone-Based Surveys: Structured Evaluation of a UAS-Based Land Survey

Burgett, Joseph M.; Lytle, Blake; Bausman, Dennis; Shaffer, S.; Stuckey, Eric

doi:10.1061/(asce)is.1943-555x.0000605

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The collinearity equations are formed by the image points, the object points, and the camera station points at the moment of image acquisition. By considering the rotation and translation of each ray in space, the external orientation parameters and the ground point coordinates are obtained through the adjustment of the image control point coordinates [17,18]. The main model of the bundle block adjustment method is the collinearity equation, as shown below:…”

Section: Aerial Triangulationmentioning

confidence: 99%

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

Li,

Peng,

Tang

et al. 2023

Buildings

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To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges.

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Section: Aerial Triangulationmentioning

confidence: 99%

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

Li,

Peng,

Tang

et al. 2023

Buildings

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“…Nonami (2016) mentioned the favorable aspect of using drone for its autonomous flying and real-time data. It is quick and has high accuracy in data collection, thus results from analysis are more precise (Burgett et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Advanced Landslide Prediction Utilising Unmanned Aerial Vehicle (UAV)

Zabidi¹,

Hadi²,

Derahman³

et al. 2022

IJARBSS

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Slope stability involves developing a terrain slope profile and determining the geometry of the failure surface to compute the slope factor of safety. Terrain capture and reconstruction traditionally has been expensive, time consuming and manpower intensive. This innovative method utilizing unmanned aerial system technology alongside the advancement in graphics processing can be proven as more effective and cost efficient. This research aims to develop a method to forecast the occurrence of landslide by utilizing drone technology. The objectives are to obtain data on terrain slope, create 3D model of the land, then perform analysis on the slope stability. Drone technology equipped with high accuracy GPS system captures high resolution aerial images of a land area for mapping. The data obtained is uploaded into computer software to generate the 3D model of the land. Then, the critical details such as contours or the elevations can be extracted. This information is transferred into an alternative software such as GeoStudio to analyze the stability of slope. Thus, the results can be used to predict the level of safety of the terrain slope and taken for further safety measures involving the particular area.

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Physical risk assessment of drone integration in construction using 4D simulation

Zhu,

Jeelani,

Gheisari

2023

Automation in Construction

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Accuracy of Drone-Based Surveys: Structured Evaluation of a UAS-Based Land Survey

Cited by 7 publications

References 9 publications

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion

Advanced Landslide Prediction Utilising Unmanned Aerial Vehicle (UAV)

Physical risk assessment of drone integration in construction using 4D simulation

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