Artificial intelligence (AI) gets a bad rap: it’s threatened by pretense and plagiarism, it’s threatened by bias and the reinforcement of systemic prejudices, it’s threatened by the automation of tasks that would otherwise provide work for humans, and much more. All of these concerns are legitimate. However, AI is also showing its value as a transformative technology that could change the way we deal with threats. One such threat is wildfires.
Traditional methods of assessing wildfire damage often rely on time-consuming and labor-intensive processes that can delay critical response efforts and prolong uncertainty for affected communities. AI-powered tools represent a paradigm shift by enabling rapid analysis of all kinds of wildfire-related data, improving the overall effectiveness of disaster response and recovery operations.
Check out these innovations, and more, below:
1. Predicting fires using hikers’ photos
NOBURN, a new mobile app, uses AI to help predict and prevent wildfires. Developed by Australian University of AdelaideThe app analyzes photos taken by hikers in fire-prone areas, assesses potential fuel loads, and predicts the severity and spread of potential fires. This crowdsourced data mimics expert assessments, enabling large-scale monitoring without the need for experts on site.
Inspired by the devastating Black Summer fires of 2019-20, NOBURN aims to raise awareness of the benefits of AI in disaster prevention and improve situational awareness for bushfire commanders. This initiative could save millions of dollars in damages and protect lives by using public participation in bushfire risk assessments.
Discover the innovative AI application
2. Precision, real-time, frontline tracking and prediction
NASA’s Wildfire Digital Twin project is expected to revolutionize wildfire management by providing real-time monitoring and forecasting tools. Funded by NASA Earth Science Technology Office and the FireSense program, this tool uses AI to predict fire trajectories and harmful air pollution, integrating data from a range of sensors and satellite images to create highly accurate models. These models, with a resolution far higher than those currently used, can be generated quickly, helping first responders in real time.
The project aims to provide firefighters with real-time information, even in remote locations without internet access. It not only helps with immediate disaster response, but also supports long-term research on global wildfire trends, particularly in carbon-rich boreal forests. By improving the accuracy and timeliness of wildfire forecasts, NASA’s technology is strengthening disaster risk reduction efforts, potentially saving lives and reducing environmental damage.
Learn how NASA is fighting fires
3. A cutting-edge solution for early detection of forest fires
Australian scientists have developed an advanced wildfire detection system using AI-enabled CubeSats – miniature cube-shaped satellites that are relatively inexpensive to produce and operate. The breakthrough can detect fires 500 times faster than traditional ground-based methods. Funded by SmartSat CRC and led by the University of South AustraliaThe Kanyini mission uses AI to analyze hyperspectral images directly in space. The onboard AI reduces the data to 16% of its original size and consumes 69% less power.
This early detection system identifies smoke before fires intensify, enabling faster response. With this innovation, bushfires can be detected within minutes, providing crucial early warnings. The team aims to have the Kanyini mission operational by 2025 and eventually commercialise the technology for wider use in disaster risk reduction.
Discover CubeSat fire detection
4. A neural network model to reduce peatland fire risks
Peatland fires are increasing, driven by changing land use and warmer, drier conditions due to climate change. Aalto University developed an AI model that can predict and reduce fires in peatlands. In a study focused on Central Kalimantan in Indonesia, the neural network model accurately predicted fires and evaluated management strategies. By analyzing land cover and pre-fire conditions, the model identified interventions to reduce fires by 50–76%.
Converting shrublands to swamp forests and blocking drainage channels have proven to be the most effective interventions, but these measures are costly. This new tool provides essential information for policymakers to implement effective disaster risk reduction measures that can protect lives and property, and reduce carbon emissions.
See AI in action to reduce peatland fire risk
5. Aerial photo analysis for rapid assessment of fire damage
After wildfires ravage villages, residents and insurers must assess property damage and begin repairs and rebuilding. Delays can prolong disruptions and lead to mounting costs. Researchers from Stanford University And Cal Poly have developed an AI tool to speed up the process by quickly identifying buildings damaged by wildfires using aerial photos. This innovation, DamageMap, enables a rapid assessment, significantly faster than the traditional method of manual inspection. By analyzing post-fire images, the platform can provide immediate information on building damage with an accuracy of over 92%.
The tool aims to assist first responders and displaced residents by identifying damaged structures, helping to speed up recovery efforts and claims. While not replacing in-person assessments, DamageMap offers a scalable solution to improve disaster risk reduction and recovery processes. The research highlights the potential of AI to improve emergency response and help communities rebuild more effectively after wildfires.
Learn about rapid fire damage assessments
A smarter toolbox for a brighter future
As wildfires intensify and become more frequent due to climate change, we need to improve our capabilities for fire prediction and prevention, resource allocation, and damage assessment. AI-based tools, such as those discussed above, hold promise for improving the speed and accuracy of wildfire risk reduction, response, and recovery.
Smart innovations and new technologies, including artificial intelligence tools, are being designed and implemented to reduce all types of disaster risks. PreventionWeb continues to analyze data to bring you the latest advances in disaster risk reduction.
