Modern utilities are leveraging AI-powered LiDAR to upgrade aging transmission networks and meet climate-driven resilience needs. LiDAR scanners capture billions of precise 3D data points (a “point cloud”) representing lines, towers, vegetation and terrain. The real breakthrough comes when AI and machine learning classify that data. By automatically distinguishing wires, poles, trees and ground, AI turns raw scans into actionable insights. For example, utilities can detect vegetation encroachment on lines and assess conductor sag or pole tilt before issues arise. This predictive maintenance approach helps crews fix problems before outages or fire risks occur, improving reliability and safety.

As traditional methods of data analysis buckle under the sheer volume of LiDAR information, AI-driven tools are essential. Deep-learning models can label powerline infrastructure in huge datasets in hours rather than weeks. This level of automation means utilities spend less time manually sorting data and more time planning strategic upgrades. The result is a smarter grid: faster risk detection, more accurate clearance checks, and proactive upgrades guided by real-time analytics. By combining LiDAR and AI, line contractors and utilities gain a detailed virtual model of the network, enabling more efficient maintenance and future-proof grid expansion.