From Visibility to Insight: Turning LiDAR into a Strategic Asset with AI

Electric utilities today are managing more complex infrastructure than ever before. With rising demands for reliability, climate-driven risks, and the push toward modernization, utilities need more than visibility—they need actionable intelligence. LiDAR (Light Detection and Ranging) technology provides unparalleled data about infrastructure, but turning that raw information into useful insight requires one critical step: classification.

LiDAR scans generate billions of high-resolution data points, mapping everything from conductors and poles to surrounding vegetation and terrain. But without classification, those points are just coordinates in space. Utilities can't act on a point cloud unless they know what each point represents. That’s where classification becomes a strategic differentiator.

Why Classification Unlocks the Value of LiDAR

Classification is the process of assigning labels to individual points—identifying which ones are wires, which are trees, and which belong to the ground or built structures. For electric utilities, this is the foundation of many critical operations:

• Detecting vegetation encroachment that could lead to outages or wildfires
• Assessing infrastructure health, such as wire sag or pole tilt
• Ensuring regulatory clearance compliance
• Planning upgrades and designing new systems
• Prioritizing maintenance in high-risk areas

Without accurate classification, even the most detailed LiDAR scan is just noise.

The Problem with Traditional Classification Workflows

Historically, utilities relied on rule-based methods and manual annotation to classify point clouds. This approach may work for small-scale or simple terrain, but it quickly breaks down at utility scale. Datasets can span hundreds of miles and include billions of points. Manual processing is slow, expensive, and difficult to standardize.

In many cases, classification work is outsourced overseas, which may cut costs but introduces concerns around data security and quality control. Furthermore, traditional algorithms often struggle in dense vegetation, urban clutter, or areas with overlapping infrastructure, leading to errors and missed risks.

How AI Is Transforming LiDAR Classification

Artificial intelligence, especially deep learning, is changing the equation. AI models trained on labeled point cloud data can automatically identify features with high accuracy. These systems don’t rely on rigid rules; they learn complex patterns in geometry and reflectivity to make nuanced distinctions between similar shapes.

For utilities, that means:

• Faster classification of massive datasets
• Improved accuracy in cluttered or variable environments
• Reduced need for manual correction
• The ability to keep classification in-house with secure, repeatable processes

AI also supports automated QA/QC processes, helping flag anomalies during classification. Combined with visualization tools, AI-driven workflows provide rich, interactive 3D models that give utility teams a clear, actionable view of the grid.

Choosing the Right Tool for the Job

Not all LiDAR classification tools are created equal. Some prioritize speed but sacrifice precision. Others offer control but are too slow for real-world deployment. For electric utilities, the ideal solution includes:

• Fully automated AI classification with support for key asset types
• Hybrid workflows that allow for manual refinement
• Feature extraction for poles, conductors, and vegetation
• Built-in quality checks to ensure data integrity
• Integration with asset management and GIS systems

Utilities should also look for systems that support dynamic updates, allowing new data to be added without restarting the entire classification process. That keeps models fresh and reduces turnaround time.

Looking Ahead: AI, LiDAR, and the Future of the Grid

The role of LiDAR is evolving. As AI capabilities grow, classification will become a near real-time process, with models deployed on drones, vehicles, or mobile devices. Predictive analytics will become possible as historical point clouds are used to forecast risk areas, such as vegetation growth or structural fatigue.

Eventually, LiDAR classification will become a standard utility function, not a specialized task. AI will enable utilities of all sizes to access fast, affordable insights from their data—fueling better decisions, smarter infrastructure planning, and a more resilient electric grid.

LiDAR gives utilities a powerful tool to see their infrastructure, but classification turns that vision into strategy. AI removes the barriers of scale, speed, and complexity that have long plagued traditional workflows. With the right tools in place, utilities can turn raw data into precise, predictive insights—supporting a smarter, safer, and more proactive approach to grid management.

Editor's Note: Author James Conlin will be co-presenting at this year's T&D World Live conference and exhibition on Thursday, Sept 25 at 9:45 a.m. on Revolutionizing LiDAR Classification with Advanced AI Algorithms at PG&E. T&D World readers will receive a 20% discount off of registration with code TDWREADER.