摘要： For periodic monitoring of power utilities, there has been keen interest by utility companies to extract the powerlines from laser scanning data. However, challenges arise when utilizing large point clouds as well as avoiding false positives or other errors in the extraction due to noise from objects in close proximity to the powerlines. In this study, we propose an efficient and robust approach to overcome these challenges through two main steps: candidate powerline point extraction and refinement. In the candidate powerline point extraction step, a voxel-based subsampling structure temporarily substitutes the original scan points with regularly spaced subsampled points that still preserve key details present within the point cloud but significantly reduce the dataset size. After removing the ground surface and adjacent objects, candidate powerline points are efficiently extracted through a hierarchical, feature-based filtering process. In the refinement step, the link between the subsampled candidate powerline points and original scan point cloud enable the original points to be segmented and grouped into clusters. By fitting mathematical models, an individual powerline is re-clustered and used to reconstruct the broken sections in the powerlines. The proposed approach is evaluated on 30 unique datasets with different powerline configurations acquired at five different sites by either a terrestrial or mobile laser scanning system. The parameters are optimized through a sensitivity analysis with pointwise comparison between the extracted powerlines and ground truth using 10 diverse datasets, demonstrating that only one requisite parameter varied as a function of resolution while the remaining parameters were generally consistent across the datasets. With optimized parameters, the proposed approach achieved F1 scores of 88.87–95.47% with high efficiency ranging from 0.81 and 1.46 million points/sec when tested on 30 datasets.