Lightweight Unmanned Aerial Vehicle (UAV) for 3D topographic mapping in mining industry has been raised significantly in recent years. Especially, in complex terrains such as in open-pit mines in which the elevation is rapidly undulating, UAV-based mapping has proven its economical efficiency and higher safety compared to the conventional methods. However, one of the most important factors in UAV mapping of complex terrain is the flight altitude, which needs to be considered seriously because of the safety and accuracy of generated DEMs. This paper aims to evaluate the influence of the flight height on the accuracy of DEMs generated in open-pit mines. To this end, the study area is selected in a quarry with a complex terrain, which is located in northern Vietnam. The investigation was conducted with five flight heights of 50 m, 100 m, 150 m, 200 m, and 250 m. To assess the accuracy of resulting DEMs, ten ground control points (GCPs), and 385 checkpoints measured by both GNSS/RTK and total station methods were used. The accuracy of DEM was assessed by root-mean-square error (RMSE) in X, Y, Z, XY, and XYZ components. The results show that DEM models generated at the flight heights of less than 150 m have high accuracy. RMSEs of the 10 GCPs increase from 1.8 cm to 6.2 cm for the vertical (Z), and from 2.6 cm to 6.3 cm for the horizontal (XY), whereas RMSE of 385 checkpoints increase gradually from 0.05 m to 0.15 m for the vertical (Z) when the flight height increases from 50 m to 250 m.
Recently, in Vietnam, the detection of geodetic measurements that contain rough errors as well as such data processing method has been considered as a key step in geodetic data processing, especially for large geodetic networks with many different types of measurements like 3D - Global Navigation Satellite Systems (GNSS) network. On the other hand, mines in Vietnam often have complex terrains, so it is necessary to apply modern and flexible surveying methods in combination with ground and space measurements to build 3D coordinates control networks for management and exploitation to ensure sustainable development. Therefore, this research developed a Robust estimation method based on empirical weighting function for establishing 3D geodetic network combining terrestrial observation and GNSS vectors. The experiment on processing the combined network in Lang Son limestone quarry, Vietnam showed that the proposed method could be an effective solution for processing 3D terrestrial – GNSS geodetic network for mine surveying in Vietnam.
Based on the geological and mining conditions of face 3107 at Liang Baoshi coal mine, China, the numerical programs FLAC3D 2.10 and PFC2D 2.10 were used to analyze the parameters controlling the failure, caving and the coal recovery rate in Strip Longwall Top Coal Caving (SLTCC). The analyzed parameters are face length in dip direction, mining height, the span of coal caving, and sequence of coal drawing. The results show that the application of SLTCC for a limited face length is not favourable to coal failure, and it increases top coal loss. A sound engineering selection of technological parameters is therefore important to efficient mining in thick coal seams. The numerical results show that a face design of 3 m of cutting height, 0.8 m of caving span, and alternate drawing sequence results in high coal recovery rate, simple mining tasks, and efficient operation of face equipment.
Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets. Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment. Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings. Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings. Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.
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