Abstract

The rapid development of digital twin technology has opened new avenues for infrastructure management, particularly for addressing vegetation encroachment risks near power lines. This paper builds upon our previous work in LiDAR-based proximity detection by proposing a framework for creating digital twin for vegetation management near power distribution networks. The framework leverages the RandLA-Net model for semantic segmentation of power lines, poles, and vegetation followed by clustering and rule-based thresholding for data refinement. Detecting vegetation encroachment is achieved through KDTree-based spatial analysis, ensuring efficient identification of risk zones. The segmented and processed point cloud data is then transformed into detailed 3D models, forming the basis of the digital twin, which can be enhanced in the future by adding advanced semantic attributes and predictive tree growth models, enabling proactive vegetation management. The methodology is demonstrated through a case study, highlighting its potential to enhance operational efficiency and the resilience of power distribution networks.