Vegetation encroachment is a major issue to the reliability of overhead power distribution networks, particularly in Brazilian urban areas where networks maintenance is still largely manual and the management uses the reactive approach instead of proactive solutions. This paper presents a decision-rule-based pipeline designed to automatically detect overhead power lines and vegetation contact areas using high-density mobile LiDAR data in Brazilian urban environments. The proposed method classifies point clouds into four primary classes: low-voltage cables, medium-voltage cables, poles, and trees in proximity to the power distribution network systems. The pipeline leverages geometric features derived from eigenvalue-based tensor analysis, height and density filters, Hough Transform, and region-growing techniques, to effectively segment and classify electric components and surrounding vegetation. The method was tested in two distinct urban scenarios, including suburban and downtown areas in Presidente Prudente, São Paulo, Brazil, with point densities exceeding 2 million points per square meter. Evaluation against reference datasets from a utility company demonstrated high precision and F-scores above 0.85 for power lines detection. Despite limitations related to parameter tuning, leak of reference data for tree detection evaluation, the pipeline offers a promising approach for semi-automatic annotation of LiDAR datasets. This process can support future applications in deep learning model training for urban asset monitoring and vegetation management. It is suggested that future works focus on reducing parameter dependency and enhancing vegetation classification reliability.