Prevent Pole Fires. Protect Infrastructure. Save Lives.

AI-Driven Wildfire Risk Mitigation for Utility Companies

We help utility companies prevent disasters with our AI-powered wildfire risk mitigation solution, already deployed for major organizations like Southern California Edison and Microsoft.

Our Solution: AI + Drones for Preventive Equipment Monitoring

Across the U.S., over 3,000 utility pole fires occur annually, often caused by undetected vegetation encroachment, cracks, sparks, corrosion, and damages. With each incident costing utilities an average of $25,000 per pole, the financial toll is steep.
But beyond the dollars, the stakes are far higher: wildfires threaten lives, homes, and ecosystems.

Our end-to-end Wildfire Risk Mitigation Solution combines drone or third-party-provided images and advanced AI image processing to proactively identify pole vulnerabilities, before they ignite fires.

Key Capabilities of Our AI Solution

Delivering Results for Real-World Use Cases of Electric Utilities

Whether your poles serve urban grids or wildfire-prone rural networks, our AI solution adapts to any terrain and inspection strategy.

  • Distribution Line Monitoring

  • Substation Asset Inspection

  • Vegetation Encroachment Detection

  • Remote Infrastructure Monitoring in High-Risk Zones

Case Study

Developed Solution for Southern California Edison

SCE

We built an end-to-end platform that picks up third-party (Drones) provided images of electric poles to detect the quality of the image for further downstream consumptions. Once the quality of the images were determined to be adequate, the platform channeled these images for anomaly detection and other downstream analytics.

The goal was to develop the complex machine learning models and meeting the performance/accuracy metrics. ThirdEye Data had successfully delivered the machine learning (computer vision) models to automate the image quality assessment for Field Asset Inspection (part of Wildfire Mitigation Program) and met model performance metric targets. The company deployed the models into production and started using the systems seamlessly.

Case Study

Developed Solution for Microsoft

Client: Microsoft

We are a long-time partner of Microsoft, developing AI solutions for enterprises by leveraging Microsoft technology stack. For this project, we collaborated with Microsoft to build a scalable AI platform that analyzes drone imagery for utility infrastructure health monitoring, integrated with Azure for enterprise-level deployment.

ThirdEye Data developed and deployed an AI-powered computer vision solution to detect anomalies in electric poles and predict potential failures that could result in service disruptions or wildfires. By analyzing drone-captured images, the system identifies defects in poles and their subcomponents, enabling preventive action. This solution is integrated into a larger platform used by field teams, customers, and partners of a global IT enterprise, ensuring safety, reliability, and operational efficiency.

Optimize Wildfire Risk Mitigation Program with AI

Leverage our hands-on experience, to mitigate wildfire threats to human lives and critical infrastructure with AI-driven solution.

Please Share Your Requirements to Get Started

Thought Leadership Article

How AI-Powered Preventive Maintenance Saves Lives, Time And Money For The Utility Sector

Across the U.S., there are 3,000 incidents of utility poles catching fire each year, many of which are caused by leakages and damages to a pole that went unaddressed for too long. Such damages can be hugely expensive when not dealt with: The average cost of a pole fire on a distribution line is $25,000. But it’s not just money that’s saved when pole fires are avoided — it’s lives and homes, too.

There are news reports that wildfires that start or spread in California could be attributed to utility companies and their equipment. According to an L.A. Times article, electrical power problems sparked fires that spanned 149,241 acres in California in 2015 (the last year of reported data).

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