New AI Technology in the Fire Service

Discover how AI is transforming the fire service with smarter response tools, predictive analytics, firefighter safety, health, and real-time incident support.

We have all been hearing more about “artificial intelligence” and “machine learning” lately. These terms are everywhere, but what do they actually mean, especially for the fire service?

First, Artificial Intelligence (AI) is simply the ability of computer systems to perform tasks that normally require human intelligence. This could be anything from recognizing faces in photos to understanding spoken commands to making decisions based on complex information. Think of AI as the broad umbrella term for machines doing “smart” things.

Machine Learning (ML) is a specific type of AI, and it’s what’s driving most of the AI applications you hear about today. Here’s the key difference: traditional computer programs follow explicit rules that humans write (“if the temperature exceeds 150°F, trigger an alarm”). Machine learning, on the other hand, learns patterns from examples. You feed it lots of data—say, thousands of images of smoke conditions—and it figures out on its own how to recognize smoke in new images it’s never seen before.

How AI is Used in the Fire Service

AI has expanded its use within the fire service in recent years, with developments in technology that can be utilized in areas such as preplanning, fire prediction, incident response, and health and safety.

This article discusses some of the recent developments and new technologies that have already been incorporated into fire service agencies around the country, as well as cautions and considerations for integrating new AI technologies.

Using AI in Wildfire Preplanning and Prediction

Fire prediction tools are used in preplanning to provide forecasts on fire danger, occurrence, behavior, fuels, and assets needed. Current systems can provide information to fire personnel on wildfire preparedness levels, mapping, satellite imagery, firefighting assets, fire situations, fire potential, risk level, climate, weather, and smoke.¹ AI can be used to enhance the abilities of these forecasting tools.

Predictive Modeling for Fire Behavior and Spread

AI and MI can help to improve wildfire forecasting abilities by resolving limitations that affect traditional wildfire models. AI models integrate data more quickly, so more data can be used to continuouslyupdate wildfire forecasts. AI can also recognize data that’s potentially inaccurate more quickly, and use prior information for modeling in situations where new data is insufficient or does not exist.²

In a 2024 study, USC researchers shared a newly developed wildfire forecasting model that uses generative AI in combination with satellite data to accurately predict wildfire spread. Satellite data used to track the progression of a wildfire in real-time was entered into a computer algorithm, which was able to successfully predict the likely intensity, path, and growth rate of the fire.³ The researchers’ data analysis showed how fire patterns were impacted by factors such as fuel, weather, and terrain.

Integrating AI with GIS, Drone, and Building Data

AI smart sensors and cameras can be used in conjunction with drones in order to analyze real-time data at fire scenes and prescribed burns more efficiently. AI models can incorporate geographic information, wind, temperature, moisture, and other environmental factors, and other data available in order to help fire personnel on the ground with data-supported decision-making.⁴

Machine Learning Tools for Risk-Based Response Plans

Recent advancements in ML have significantly helped to improve fire risk assessment and fire safety. ML frameworks can utilize data such as fire proximity, impact, probability, and response, to predict things like fire incidents, fire risk, and fire development. Research continues to develop new ML models and frameworks that can be used in a variety of fire service settings to assist in response planning.⁵

AI for Incident Command and Post-Incident Analysis

AI and ML can be useful for Incident Command Systems and Post-Incident Analysis. Current integrations include AI dashboards and Machine Learning frameworks that can assist in decision-making for multi-agency responses, incident command, and dispatch.  

Dashboards for Multi-Agency Response

AI dashboards can be a helpful tool for agencies that need to collaborate for emergency responses. AI dashboards can include data such as station location, incident location, incident type, time to dispatch, time to arrival, time to en route, total callouts, and callouts on target or off target. This data can be summarized and analyzed into easily understood metrics, providing valuable insight for emergency response teams.⁶

AI-Driven Decision Support for ICS and Dispatch

AI and ML can be used to assess fire risk and predict the best emergency response. AI models can include proximity and performanceof fire response which can support decision making for Incident Command Systems and dispatchers. ML frameworks can be used for allocating resources, and strategizing and improving incident response.⁵

Using AI in Wearable Technology for Firefighters

AI can be used in a variety of ways in wearable technology. Possibilities for wearable AI include smart watches and rings for personal health and safety, AI-integrated SCBA equipment, and AI helmets for victim rescues.

Wearable Tech for Real-Time Vital Signs Monitoring

A common form of AI in wearable tech is found in smart watches or smart rings. These items can be used to continuously monitor vital signs such as heart rate, respirations, temperature, blood oxygen, and sleep data.⁷ Features on certain watches can also be enabled to check for heartbeat irregularities, and alert authorities in the event of sudden falls.

Heat Exposure and Overexertion Tools

Smart watches and rings have the ability to identify when temperature is elevated such as during exposure to intense heat, and can use a variety of data points to analyze physiological stress. Health data can also be used to predict how a firefighter can best recover and increase resilience following periods of stress.⁸

AI Powered SCBA Systems

Utilizing AI in SCBA technology is an emerging trend. Integrating AI into SCBA equipment allows for real-time sensor data analysis in greater detail, which can help firefighters make fact-based decisions and improve safety in hazardous situations.⁹

AI Integrated Wearable Uniform Tech

Researchers in Scotland, in collaboration with the Scottish Fire and Rescue Service, developed a smart helmet with AI technology that can help firefighters locate fire victims more quickly. The helmet uses thermal cameras, sensors, and radar which can help firefighters to map their environment in smoke-filled surroundings. Researchers claim the helmet can scan a room in five to ten seconds, when it would take a human several minutes, which would greatly expedite locating and rescuing victims.¹⁰

Challenges and Considerations for Fire Chiefs

While Artificial Intelligence has the potential to improve and benefit many areas of the fire service, it is important to proceed with caution. Research any AI technology extensively before purchasing and implementing it in your department.

Compare potential risks and negative outcomes, with the expected benefitsof using AI in certain situations. With all of the benefits that AI brings, there are some things that it cannot replace. There should always be human oversight and human involvement with any AI technology utilized.

FAQs

• How is AI currently being used in the fire service?
  • AI is currently being used in a variety of areas of the fire service, including preplanning, predictive modeling, incident response, incident command, and firefighter health and safety.
• Can AI Technology help protect firefighter health?
  • Through wearable technology, AI can help give firefighters insights into their health and suggestions for recovery and resiliency.
• What are the biggest challenges of adopting AI in fire departments?
  • AI is an exciting addition to many areas of the fire service, but like any new technology, it must be implemented with caution and appropriate oversight. It is not without error and human involvement is still essential.