James Conlin is a director of product for Sharper Shape.
As power grids grow older and climate threats intensify, electric utilities face urgent pressure to modernize. Meeting today’s expectations for resilience, safety, and efficiency depends not just on upgrading physical infrastructure, but on having the right data — accurate, timely, and scalable insights into assets across vast and varied terrain.
One technology is rapidly changing how utilities manage their infrastructure: LiDAR (Light Detection and Ranging).
LiDAR captures millions to billions of precise, high-resolution 3D data points — forming what’s known as a point cloud. These point clouds create detailed digital models of utility networks and their surrounding environments, mapping everything from power lines and substations to terrain and vegetation. This level of visibility is critical for planning, maintenance, risk mitigation, and emergency response.
But collecting LiDAR data is only the beginning. The real value comes from turning that data into something useful. That’s where classification comes in.
Raw LiDAR point clouds are essentially unstructured spatial data. Each point marks a location in space but offers no context on its own. Is it part of a wire, a tree, or the ground? Without classification, there’s no way to know. Classification assigns meaning to these points by labeling them according to what they represent, transforming raw data into actionable information.
For electric utilities, this process is essential. It enables vegetation management by identifying growth that’s encroaching on power lines before it becomes a hazard. It supports asset inspection by helping monitor conditions such as wire sag, pole tilt, or equipment degradation. It ensures compliance and safety by verifying that infrastructure meets required regulatory clearances. It aids in disaster modeling by identifying potential risk zones for wildfires, floods, or storms. And it guides system upgrades by informing the design of new infrastructure or the expansion of existing networks.
In short, without classification, even the highest-resolution LiDAR data remains unusable. Real insight only emerges when this cloud of points is organized, labeled, and understood.
Traditional methods are no longer enough
Historically, classification has relied on rule-based systems, statistical models, and manual workflows. Analysts would apply filters, segmentation tools, and human judgment to distinguish between features.
While this approach can work in limited contexts — such as small, flat, or sparsely populated areas — it breaks down at scale. Today’s LiDAR datasets can cover hundreds of square miles and include billions of points. Manually classifying that volume of data is not only labor-intensive; it’s cost prohibitive. Even semi-automated approaches often require extensive review and corrections, especially in complex terrain.
Utilities are hitting the ceiling of what traditional classification methods can deliver. Enter artificial intelligence.
Artificial intelligence, particularly deep learning, has fundamentally transformed how LiDAR data is interpreted. Unlike traditional rule-based systems, AI models learn from examples, recognizing patterns across massive datasets and automatically classifying points with remarkable accuracy.
One of the key strengths of deep learning is its ability to distinguish between similar shapes, such as differentiating wires from nearby branches. It can also detect small or partially obscured infrastructure components that might be missed by manual methods. These models adapt to a wide range of environments without requiring significant reprogramming, making them flexible across diverse terrain and utility networks. Most importantly, they scale effortlessly — processing massive datasets in hours rather than weeks.
For utilities, the impact is significant: faster analysis, greater precision, and more reliable insights with less dependence on human intervention.
Selecting the right tools
Not all LiDAR classification tools are created equal. Some rely heavily on automation and AI, while others still depend on manual or semi-automated workflows. Choosing the right solution depends on project needs: the scale of the data, the complexity of the landscape, and the required level of accuracy.
AI-based tools offer the advantage of speed and scalability. Many can automatically classify key elements — such as ground, vegetation, wires, and poles — across extensive datasets. More advanced tools include feature extraction capabilities that allow utilities to dig deeper, modeling clearances, detecting anomalies, or assessing degradation over time.
That said, fully automated tools aren’t perfect. Complex or cluttered environments can trip up even the best algorithms. For this reason, hybrid workflows, where AI handles the bulk of the work and human experts step in for fine-tuning, remain the most effective approach in many cases.
Other critical features to look for include robust QA/QC tools to flag inconsistencies, visualization tools for reviewing classification in 3D, and seamless integration with existing GIS and asset management systems.
Despite its promise, AI-based classification isn’t without hurdles. One of the biggest is the need for training data: lots of it, accurately labeled. Creating high-quality datasets specific to utility infrastructure is time-consuming and expensive.
Another limitation is generalizability. A model trained in an urban setting might perform poorly in a forested or mountainous region. Retraining models for different geographies often require specialized expertise.
Additionally, some features, such as rare infrastructure types or unusual damage conditions, still require manual identification or custom software solutions. While general-purpose AI tools are becoming more accessible, utility-specific challenges still demand tailored approaches.
And yet, progress continues. Modern workflows now include automated point cloud alignment and registration, significantly reducing setup time. AI models can be updated dynamically as new data is collected, avoiding the need to reprocess entire datasets. Many tools also include built-in QA/QC that automatically flag outliers or inconsistencies for review.
The result: cleaner, faster, and more reliable classification — with fewer bottlenecks.
Looking ahead: What’s next for AI+LiDAR in utilities
The future of utility-grade LiDAR is tightly linked to advances in AI. As models become more sophisticated and tools more intuitive, classification will shift from a manual task to a seamless part of the data pipeline.
Emerging capabilities are already on the horizon. Predictive modeling, for example, will use historical LiDAR data to forecast vegetation growth, detect early signs of infrastructure fatigue, or assess changing fire risk. Real-time classification, powered by edge processing on drones or mobile devices, will enable utilities to make faster field decisions without waiting for central processing.
Equally important, LiDAR data is increasingly being integrated with broader asset systems: GIS platforms, inspection tools, and maintenance databases, creating a unified view of infrastructure that supports proactive management.
As costs drop and tools mature, these capabilities won’t be limited to the largest utilities. Smaller co-ops, municipal providers, and regional operators will gain access to the same tools, leveling the playing field and accelerating grid modernization across the board.
AI-powered LiDAR classification is no longer a high-tech novelty — it’s rapidly becoming a critical component of modern utility operations. Faced with aging infrastructure, increasing environmental threats, and tightening budgets, utilities need smarter tools to manage assets and mitigate risk.
With scalable, intelligent classification, utilities can move beyond reactive maintenance and into predictive, data-driven decision-making. The benefits are clear: improved reliability, faster emergency response, safer operations, and lower overall costs.
This isn’t just a better way to analyze data — it’s a smarter way to build and operate the grid.