Artificial intelligence (AI) has gotten a bad rap: threats to authenticity through fakes and plagiarism, threats to fairness through systemic bias and the reinforcement of systemic bias, threats to the workforce through the automation of tasks that would otherwise provide human jobs, 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 manage threats. One of these threats is wildfires.
Traditional methods of assessing fire damage often rely on time-consuming and labor-intensive processes that can delay critical response efforts and prolong uncertainty for affected communities. AI-based tools represent a paradigm shift in enabling rapid analysis of all kinds of wildfire-related data, thereby improving the overall efficiency of disaster response and recovery operations.
Discover these innovations, and more, below:
1. Predict fires with hiker photos
NOBURN, a new mobile phone app, uses AI to help predict and prevent wildfires. Developed by Australians University of AdelaideThe app can analyze photos taken by hikers in fire-prone areas, assess potential fuel loads, and predict the severity and spread of possible fires. This crowdsourced data mimics expert assessments, enabling widespread monitoring without the need for on-site experts.
Inspired by the devastating fires of the Black Summer of 2019-2020, NOBURN aims to raise awareness of the benefits of AI in disaster prevention and improve the situational awareness of bushfire commanders. This initiative could prevent millions in damage and protect lives by using public participation in bushfire risk assessment.
Explore the innovative AI application
2. Accurate, real-time, frontline monitoring and forecasting
NASA’s Wildfire Digital Twin project is poised 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 much higher resolution than those currently in use, can be generated quickly, helping first responders in real time.
The project aims to provide firefighters with timely information, even in remote locations without internet access. In addition to helping to respond immediately to disasters, it also supports long-term research into global wildfire trends, particularly in carbon-rich boreal forests. By improving the accuracy and speed of wildfire forecasts, NASA technology enhances disaster risk reduction efforts, potentially saving lives and reducing environmental damage.
Learn how NASA is fighting fires
3. A space-age solution for early detection of wildfires
Australian scientists have developed an advanced forest fire detection system using AI-equipped miniature CubeSats that are relatively inexpensive to produce and operate. This advancement makes it possible to detect fires 500 times faster than traditional land-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. Onboard AI reduces data to 16% of its original size and uses 69% less energy.
This early detection system identifies smoke before fires intensify, allowing for faster responses. With this innovation, bushfires can be detected within minutes, providing crucial early warnings. The team aims to operationalize the Kanyini mission by 2025 and ultimately commercialize the technology for wider use in disaster risk reduction.
Discover CubeSat fire detection
4. A neural network model to reduce fire risks in peatlands
Peatland fires are increasing due to land use change and warmer, drier conditions due to climate change. Researchers in Finland Aalto University designed 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 to 76 percent.
Converting shrublands to swamp forests and blocking drainage canals 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, potentially protecting lives, property and reducing carbon emissions.
See AI in action to reduce fire risks in peatlands
5. Analysis of aerial photos for rapid assessment of fire damage
After wildfires ravage settlements, residents and insurers must assess property damage and begin repairs and rebuilding. Any delays can prolong disruption and result in increasing costs. Researchers at 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, allows for rapid assessment, significantly faster than the traditional manual inspection method. By analyzing post-fire images, the platform can provide immediate information on building damage with over 92% accuracy.
This tool aims to help first responders and displaced residents by identifying damaged structures, facilitating recovery efforts and faster insurance claims. Without 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 responses and help communities rebuild more effectively after wildfires.
Discover quick fire damage assessments
A smarter toolbox for a bright future
As wildfires intensify and become more frequent due to climate change, we must improve our ability to predict and avoid fires, allocate resources, and assess damage. 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 AI tools, are being designed and implemented to reduce all kinds of disaster risks. PreventionWeb continues to analyze data to bring you the latest developments on the frontiers of disaster risk reduction.
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