How Utility Vegetation Management Software Mitigates Wildfire Risk

We’ve talked about GridOS® Visual Intelligence many times on this blog, mostly for its value in utility vegetation management (UVM), and more recently as a tool for assessing storm damages.

Today I wanted to talk about another Visual Intelligence use case that many utilities are unaware of.

That use case is wildfire mitigation.

While they may not happen as often as severe storms, floods, and the like, wildfires have always posed a risk to grids. But utilities rarely have treated them quite as seriously as extreme weather.

All that changed in 2024.

The world saw wildfires in a completely new light. We’d all seen news footage of forests on fire every summer, perhaps with a (very) rural home or two ablaze. But in 2024, news footage showed urban areas on fire. Wildfires spread across populated areas of Maui, driving residents to the coast and forcing them to jump into the ocean with their families, pets, and belongings to escape the flames. And we also saw one of the largest, most heavily populated urban areas in America, the city of Los Angeles, in the middle of an inferno.

The Los Angeles wildfire was especially disturbing, given that one of the most-affected areas was a densely packed metropolitan area. Office buildings, apartments, front lawns, and shopping centers went up in flames – as did all the grid infrastructure serving them. These were not the “wildfires” we were used to seeing – these were wildfires that spread into urban areas, decimating property and public infrastructure alike.

At our GridOS user conference in June, Orchestrate 2025, wildfire risk was a major topic of conversation. So many utility professionals shared that their organizations were planning to invest in solutions that could identify wildfire-prone areas and target them for intervention. Even utilities in states not widely known for wildfires, like Kentucky and Florida, expressed their heightened anxieties about a wildfire striking the heart of their operations and wiping out their ability to deliver power.

Visual Intelligence is an invaluable solution for wildfire mitigation. And here’s how.

As I’ve explained in past blogs, Visual Intelligence is perhaps best-known as a UVM solution. It increases the efficiency, effectiveness, and precision of UVM efforts by showing utilities and trimming crews the exact locations where overgrown or unstable vegetation could fall and damage grid infrastructure. It does this by overlaying vegetation scans with network maps, and using a simple color-coding scheme to flag trimming targets.

Utilities can also use Visual Intelligence to identify wildfire risks without doing anything differently than they already are. Sometimes, the exact same vegetation that could fall and damage power assets (think dead, unstable, or overgrown trees and bushes) could also serve as kindling in the event of a wildfire, spreading the flames directly onto your critical assets.

For example, take a look at the below Visual Intelligence screenshot, showing a length of rural power lines:

With the help of a simple white box, trimming crews can instantly see where trees are encroaching on the power lines. Intervention will be faster, easier, and more accurate.

Or consider this satellite image, which was fed into Visual Intelligence to assess the health of the pictured trees:

Visual Intelligence can also identify wildfire risk in the form of anomalies that are less noticeable than an entire tree. Fallen branches, overgrown bushes and shrubs, or even dead grass can all ignite and spread a fire towards grid infrastructure. Visual Intelligence can easily identify any of these risk factors and alert trimming crews to intervene.

That is evident in this next image, depicting another Visual Intelligence screenshot of vegetation growing around and beneath a set of transmission towers.

Two areas of concern are shown in blue. At bottom right, the foliage of a tall tree is colored blue, indicating protruding branches that are getting dangerously close to the power lines and the towers supporting them. Meanwhile, at center left, more vegetation is colored blue – but this vegetation is clearly not as tall as the problematic tree at right. This coloring indicates overgrown bushes and shrubbery that could combust during a wildfire, and must be removed promptly.

Note that the above use case involves identifying vegetation that could fuel a wildfire. Every fire starts with an initial spark. Visual Intelligence can also be used to identify potential ignition sources for a wildfire.

So we must ask ourselves, how do wildfires start?

Many are ignited by lightning or poorly extinguished campfires, neither of which we can prevent as utilities. But then there are factors we can prevent – such as those caused by damaged grid assets.

That’s where Visual Intelligence comes in again. Visual Intelligence can identify signs of physical damage to grid assets, many of which could easily spark a fire. For example, we all know electricity is a very powerful, abrasive force – especially at the very high voltages transmission lines routinely carry. Over time, those wires can weaken and fray. Frayed wires can release sparks if the conditions are just right, putting any nearby vegetation at risk of combustion. Visual Intelligence can flag fraying or damaged wires and alert the appropriate responder.

Or as another example, think about your standard transmission towers. They are typically made of galvanized steel – “galvanized” meaning that it is coated with zinc to protect it from rust and corrosion. But this is not a permanent protection – the harsh combination of wind, direct sunlight, and precipitation inevitably wear off the zinc layer over time. Even if the galvanized steel is brand new, remember that the smallest scratch could penetrate the zinc – whether caused by a falling branch, a birdstrike, hail, or another cause.

In any of these scenarios, the raw steel can be exposed to the elements and rust will result. Without intervention, the corrosion may spread and eventually compromise structural integrity. And it goes without saying that a transmission tower collapsing can easily snap power cables like dental floss and spark a serious blaze. That’s why some of our biggest customers are using Visual Intelligence to monitor their transmission towers for any signs of corrosion, cracks, or other physical damages. With its streamlined workflow and automation capabilities, utilities can identify and eliminate wildfire risk long before the first spark. It makes a world of difference in utilities’ push toward ever-greater resilience.
 
A larger strategy

Now that we’ve covered Visual Intelligence’s two workflows for identifying wildfire risk, the next question is how to use the solution’s capabilities to form a comprehensive, closed-loop risk mitigation strategy. That too is easier than one might think. Here’s how one of our top utility customers uses Visual Intelligence to identify and mitigate the risk of wildfires.

1. Satellite imagery is fed into Visual Intelligence. The utility primarily uses satellite imagery at this initial stage due to its ability to capture wide areas, including those inaccessible by field crews or drones.

2. Visual Intelligence processes the imagery and identifies areas of high concern. This can be seen in the example image below:

3. The utility examines each area and determines a cadence for follow-ups to monitor any identified wildfire risk factors.

4. The utility also runs frequent asset inspection and UVM analyses of additional imagery of the problem areas to determine the precise state of the grid. The below image shows a more detailed follow-up photo of the area flagged in the above satellite image. This new image confirms there is indeed a tree limb that has grown dangerously close to an adjacent utility pole – just as Visual Intelligence identified in the satellite photo.

5. Visual Intelligence uses any insights like the aforementioned to build a detailed and comprehensive risk map.

6. The utility uses Visual Intelligence to generate work orders dictating corrective actions (e.g. repairs, tree trimming, etc.) on identified issues to bring the risk down to zero.

It all adds up to a streamlined process for mitigating the risk of wildfires across the grid, thanks to the visual insights of Visual Intelligence.

GE Vernova recently announced its intent to acquire Alteia SAS, the software company whose technology powers GridOS Visual Intelligence. Learn how we plan to supercharge the broader GridOS portfolio with visual insights post-acquisition in this blog by Brian Hoff.