3D representation of a bauxite mine in the frame of m4mining project

Mining companies worldwide routinely monitor their excavation activity. Until a few years ago terrestrial measurements, aerial photogrammetry and remote sensing using very high-spatial resolution satellite data were the usual methodologies. In particular, executing precise terrestrial measurements with topographic equipment of Differential GNSS constitutes a time-consuming procedure. Although the absolute precision of individual points is extremely high (mm level), it is challenging to survey large land areas. At the same time, Terrestrial Laser Scanners (TLSs) provide comparable accuracy by collecting millions of points per second, decreasing the surveying time substantially; yet, deploying these sensors inside the quarries continues to be problematic. While costly, with aerial photogrammetry data from large quarry areas is collected at a cm level accuracy. Satellite data present the same pros and cons as aerial photogrammetry in terms of area coverage, accuracy, and cost. The advent of Unmanned Aerial Vehicles (UAVs) and the development of high-accuracy cameras and light-wise LiDAR sensors open new opportunities for the monitoring of quarries. In the present study we evaluate and compare the 3D point clouds derived from high-accuracy UAV cameras to the respective data collected by TLS. An open pit bauxite mine in Greece, monitored in the frame of the m4mining project, is selected as the study area. “Μ4mining” is an EU-funded project that aims at confining the resolution gap between satellite- and UAV-acquired data for mine monitoring. The 3D point clouds derived from UAV flight campaigns and TLS measurements are compared in terms of point density and fidelity of topographic representation. The current work proposes an effective and precise methodology to accurately 3D map a site, using cost-efficient data, acquired by UAV and TLS.