Evaluating UAV LiDAR and SfM-MVS Photogrammetry for Cylindrical Geometry Reconstruction of Geothermal Pipelines
Abstract
Geothermal pipeline mapping is essential for asset management and integrity monitoring, ensuring early detection of failures and supporting energy resilience. Traditional terrestrial surveys, such as total stations and GNSS-RTK, are accurate but inefficient for networks spanning tens of kilometers. Recent advances in miniaturized LiDAR and aerial cameras enable their integration with Unmanned Aerial Vehicles (UAVs), which provide dense point clouds and fine resolution through low-altitude, slow-speed flights. This study evaluates UAV LiDAR and SfM-MVS photogrammetry in reconstructing a 3 km geothermal pipeline network in Dieng, Indonesia, an area with steep terrain, fog, and active steam. LiDAR data were acquired at 2 million shots per second, while 5 MP aerial photos were processed with SfM-MVS to generate dense clouds. Geometric analysis applied the M3C2 algorithm and statistical significance testing. Findings show that both methods reconstructed semi-cylindrical geometry for matte-coated pipes, with good alignment at upper surfaces. For shiny metallic pipes, SfM-MVS failed to form coherent cylinders, producing irregular point clouds. M3C2 analysis revealed deviations <3 cm for matte pipes but >4 cm for metallic ones. Statistical tests confirmed that significant differences occurred more frequently in metallic pipes. These results demonstrate UAV LiDAR’s higher reliability for consistent cylindrical reconstruction, while SfM-MVS remains sensitive to surface reflectance.
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