AI Cameras Help Western States Detect Wildfires Earlier as Climate Risks Grow

On a March afternoon in Arizona’s Coconino National Forest, artificial intelligence spotted what looked like smoke on a camera feed. Human analysts quickly verified it wasn’t dust or clouds and alerted state forest officials and Arizona Public Service, the state’s largest electric utility. The early detection of what would become known as the Diamond Fire allowed firefighters to contain the blaze before it spread beyond 7 acres.

This success story highlights how states across the wildfire-prone West are increasingly using AI technology to detect fires earlier and protect lives and property, especially as record-breaking heat and poor snowpack raise concerns about severe wildfire seasons ahead.

Arizona Public Service has deployed nearly 40 AI smoke-detection cameras and plans to have 71 operational by the end of summer. Colorado’s Xcel Energy has already installed 126 cameras and aims to expand to seven of the eight states it serves by year’s end.

“Earlier detection means we can launch aircraft and personnel to it and keep those fires as small as we can,” said John Truett, fire management officer for the Arizona Department of Forestry and Fire Management.

The technology is proving most valuable in sparsely populated or remote areas where traditional detection methods fall short. In such locations, fires can burn longer before being spotted by passersby or traditional lookouts. California’s ALERTCalifornia network, which includes about 1,240 AI-enabled cameras, demonstrates the technology’s effectiveness.

“The AI that’s being run on the cameras is actually beating 911 calls,” said Neal Driscoll, geology and geophysics professor at the University of California, San Diego, who founded ALERTCalifornia.

Human oversight remains crucial to the system’s success. When AI flags potential smoke, human analysts review the imagery to confirm real fires and dismiss false positives. This verification process not only keeps response resources focused on actual fires but also helps train the AI systems to become more accurate over time.

Brent Pascua, battalion chief for the California Department of Forestry and Fire Protection (Cal Fire), has seen the benefits firsthand. “In many cases, we’ve started a response before 911 was even called, and in a few cases, we’ve actually started a response, went there, put the fire out, and never received a 911 call.”

Pano AI, a leading provider of this technology, combines high-definition camera feeds, satellite data, and AI monitoring. Since launching in 2020, the company has expanded to Australia, Canada, and 17 U.S. states. Last year alone, its technology detected 725 wildfires in the United States.

According to Cindy Kobold, an Arizona Public Service meteorologist, their AI system typically identifies fires about 45 minutes faster than the first 911 call—critical time that can mean the difference between a small, containable fire and a major disaster.

Arvind Satyam, Pano AI’s co-founder and chief commercial officer, explained that the technology’s development was driven by the lack of effective solutions to combat worsening wildfires. Climate change, fueled by the burning of fossil fuels, is creating warmer, drier conditions that make wildfires more frequent and intense.

Despite its promise, the technology faces challenges. At around $50,000 annually per camera (including fire risk analysis and 24/7 intelligence center services), the cost can be prohibitive for some agencies. And while AI can detect fires, it doesn’t make tactical decisions.

“Do you send help right away? Do you monitor? Should you worry about it? Where do you send help? Do you think about evacuation? All this still requires people and decision support systems,” said Patrick Roberts, a senior researcher with RAND who recently completed a project on wildfire management innovation.

The technology also has limitations in highly populated areas, where fires are typically reported quickly by residents, and during extreme weather events that cause rapid fire spread.

AI’s role in wildfire management extends beyond detection. Researchers are developing systems to identify optimal locations for vegetation thinning and controlled burns. At George Mason University, professor Chaowei “Phil” Yang is working with California researchers to create forecasting systems that predict fire movement and smoke pollution impact, potentially giving agencies vital information for evacuation decisions.

“AI in wildfires, it’s no longer just speculative. It’s really being used,” said Roberts, adding that its use will only grow. “The future is AI everywhere, and the lines will blur between AI wildfire detection and just wildfire detection as the lines will blur in other areas of our life.”

As climate change continues to intensify wildfire risks across the West, these AI systems represent an important tool in the increasingly challenging fight to protect communities and natural resources from devastating blazes.