August 2025 – Wildfires are becoming an increasingly severe global challenge, with rising temperatures, prolonged droughts, and climate change creating perfect conditions for disasters. The devastation caused by wildfires is evident worldwide: the 2019–2020 Australia Bushfires (Black Summer), the 2023 Greece wildfires in Rhodes, Canada’s 169 reported wildfires by July 2023, Turkey’s 19 wildfires since mid-July 2023, and France’s massive fire burning over 2,500 acres along the Mediterranean coast. More recently, the 2025 Los Angeles wildfire underscored the growing risks.

While wildfires have natural and human-related causes, power grids have also emerged as a significant ignition source. Grid-related wildfires can occur due to falling trees, vegetation contact, aging components, or sparks from failing infrastructure. As utilities face increasing regulatory scrutiny and public pressure to mitigate these risks, artificial intelligence (AI) and advanced data solutions are proving to be transformative.

The growing role of AI in wildfire mitigation

Last week, a new executive order from President Trump called for agencies to create a roadmap leveraging AI, data sharing, modeling, and mapping capabilities to identify wildland fire ignitions and improve weather forecasting for better emergency response. A federal fund of $3.8 billion, although mostly a reallocation of existing resources, underscores the urgency for advanced wildfire management.

This directive aligns with the growing role of technology in wildfire prevention, including AI-based grid inspections and image data that leverage vegetation management and condition-based insights to ensure the physical grid is safe and in good working order. These insights help minimize fire risk.

Federal efforts, such as the recent $3.8 billion wildfire management fund and the executive order calling for AI-driven modeling and data sharing, could make this type of image and condition data more accessible to utilities and research initiatives. By promoting standardized data-sharing frameworks, subsidizing advanced imaging technology, and supporting AI training programs, these efforts enhance AI models by giving them larger, higher-quality datasets for defect detection and risk prediction, ultimately improving accuracy and reliability.

For utilities, this policy shift emphasizes the need to go beyond traditional inspections, leveraging AI platforms like Grid Vision^® to detect early-stage defects and proactively mitigate wildfire hazards.

Why grid-related wildfires are rising

Power grids are under stress due to climate-driven heat waves, severe droughts, and increased vegetation growth. Aging infrastructure often fails to keep up with these conditions. Factors contributing to grid-related wildfires include: 

• Tree or branch contact with powerlines. 
• Exploding material or sparks from faulty components. 
• Arcing or conductor failures due to loose hardware. 
• Dry vegetation surrounding powerlines acting as fuel. 

Conductors play a key role in preventing fire hazards. To ensure safety: 

• Vegetation must be cleared around lines. 
• Conductors must remain separated using spacers. 
• Cotter pins and related hardware must remain securely fixed. 
• C-hooks and chain shackles must be inspected for wear, corrosion, or deformation. 

Loose or upside-down cotter pins and worn or damaged C-hooks compromise the safety barrier by weakening the points where conductors and insulators are secured. This increases the risk of conductors falling or generating sparks when in contact with nearby surfaces. A single cotter pin failure on a 100-foot lattice tower can have devastating consequences, and a worn C-hook can create stress points that accelerate component failure under wind or load. Yet manually inspecting 50 cotter pins and multiple C-hooks per tower is both tedious and prone to human error. 

AI-supported virtual inspections are a game-changer

The 2025 Los Angeles wildfire and the 2018 Camp Fire in Butte County, and the 2025 Eaton wildfire in Los Angeles highlight the devastating consequences of delayed or insufficient grid inspections. The Camp Fire remains the deadliest and most destructive wildfire in California’s history, underscoring why utilities must move beyond slow, manual inspection cycles.

Southern California likely has 3,500 to 4,000 lattice-steel transmission towers. Manual climbing inspections take about 2 hours per tower, meaning a single inspector would need 3.5 to 4 years to complete one full cycle. In contrast, drone-assisted inspections reduce that timeline to 7 months to 1.5 years, resulting in an efficiency gain of up to 80%.

AI-powered image analysis further accelerates the process by detecting and classifying defects within seconds, removing the need for hours of manual image review and enabling fast, data-driven maintenance decisions.

Solutions like Grid Vision® create a digital representation of the grid by linking high-resolution imagery, asset metadata, and grid topology into one platform.

Key benefits include:

• Faster defect detection and automated workflows
• Lower inspection costs and reduced manual efforts
• Safer operations by minimizing tower climbs
• Regulatory-ready, auditable visual data
• Early detection of high-risk defects, such as corrosion or faulty hardware

Case study: AI-supported inspections and advanced analytics for wildfire risk management

A leading North American utility leveraged AI-powered inspections to strengthen its wildfire prevention strategy. Using drone flights combined with AI analytics, the utility inspected 47 lattice towers and 52 wooden poles, capturing 3,465 high-resolution images in total.

AI findings:

• 781 instances of upside-down cotter pins were detected out of 11,000 cotter pin images, within seconds.
• Loose cotter pins, chain shackles, and contaminated insulators were flagged for immediate maintenance.
• Additional anomalies such as woodpecker holes, flashed insulators, and cracked poles were identified.

Project outcomes:

• The customer confirmed their grid’s good condition regarding C-hooks and shackles.
• Maintenance teams could target defect repairs without climbing towers, improving safety.
• AI’s speed and accuracy allowed the customer to proactively correct wildfire-relevant defects, preventing potential ignition events.

Vegetation management and conditional data

Vegetation is a primary fuel source for grid-related fires. AI-powered platforms use high-resolution imagery combined with conditional data from inspections to monitor vegetation encroachment and assess the overall health of grid assets.

Grid Vision® includes a “Vegetation Right of Way” filter that allows utilities to:

• Identify assets in densely vegetated areas.
• Pre-plan field work, reducing time and site visits.
• Assess site access needs before sending maintenance crews.

By combining vegetation data with condition-based insights, utilities can create a more accurate view of wildfire risks and take proactive measures to mitigate them.

Beyond inspections: predictive insights

AI-driven platforms offer more than just defect detection. With historical data and year-over-year comparisons, utilities can identify patterns that may increase wildfire risk, such as:

• Wooden poles rotting near water due to humidity.
• Asset degradation based on age or material type.
• Recurring vulnerabilities across similar asset classes.

The holistic asset data in Grid Vision® enables utilities to overlay additional datasets, such as roads, gas pipelines, or water bodies, onto their grid map. This contextual data supports better planning, risk prioritization, and faster incident response.

Common vulnerabilities and incident investigations

When a failure occurs, such as an MV substation outage, traditional utilities may lack recent, high-quality imagery to reconstruct the cause.

With AI-powered inspections, utilities gain:

• Accurate, structured visual data of substations and transmission lines.
• A full audit trail for regulatory reporting.
• The ability to conduct root cause analysis and prevent future incidents.

For example, one utility used inspection data to locate and replace a lightning arrestor flagged by regulators as a fire hazard. Without the AI-driven inventory, they would not have known where these components were installed.

Federal and industry momentum

The $3.8 billion federal initiative and policy emphasis on AI highlight the government’s recognition that data-driven approaches are critical to wildfire mitigation. This is not just a challenge for California but a national and global issue.

As utilities seek to modernize operations, partnerships between AI inspection providers, satellite imaging companies, and wildfire modeling solutions will play a critical role. Platforms like Grid Vision® can provide ignition source risk data, feeding into larger wildfire prevention ecosystems.

Key steps utilities can take now

In summary

Wildfires are no longer an isolated risk, they are a global crisis intensified by climate change and aging grid infrastructure. AI-powered tools like Grid Vision® give utilities the ability to detect 60 percent more defects than traditional inspections, reduce operational costs, and most importantly, prevent fire ignitions before they happen.

From cotter pin detection to vegetation monitoring, virtual inspections create a digital twin of the grid, enabling utilities to compare asset conditions over time, plan maintenance more effectively, and improve safety for both workers and communities.

The message is clear: AI and data-driven insights are the future of wildfire prevention. With federal funding and policy momentum supporting innovation, now is the time for utilities to embrace technology that will save time, reduce costs, and protect lives and infrastructure from catastrophic fires.