Computer science experts have introduced a new crowdsourcing system that dramatically reduces the time needed to map wildfires from hours to mere seconds. This innovative system utilizes a network of low-cost mobile phones installed on properties in areas that are highly susceptible to fires. In computer-based simulations, the system, named FireLoc, was able to detect fires starting up to 3,000 feet away and accurately chart wilderness fires within 180 feet of their origin.
The devastating wildfire in Lahaina, Hawaii, in 2023, which resulted in over 100 fatalities and scorched 6,500 acres across Maui, exemplifies how quickly wildfires can spread, complicating effective response efforts and leading to significant loss.
Could technology provide an earlier warning for wildfires? The answer might just be in your pocket: your mobile phone.
Researchers from USC have crafted a new crowdsourcing mechanism that cuts wildfire mapping time drastically, from hours down to seconds. This is achieved by deploying an array of low-cost mobile phones strategically positioned on properties in fire-prone regions. Simulations show that FireLoc is capable of identifying flames igniting as far as 3,000 feet away and mapping fire locations with remarkable precision, to within 180 feet of the fire’s origin.
Quick Detection of Wildfires
Reported at the ACM SenSys conference on November 5, the research paper titled “FireLoc: Low-latency Multi-modal Wildfire Geolocation,” showcases the concept’s potential, though researchers emphasize its need for real-world application testing.
According to researcher Xiao Fu, “It’s a valuable initial step toward comprehensive wildfire management in the future.”
The setup is user-friendly. Residents and businesses located in high-risk areas can install a cost-effective, weather-resistant mobile phone in their yard or on their building, connecting it to a power source and directing the camera towards nearby vegetation.
Once installed, intricate multi-modal analysis and computer vision techniques work behind the scenes to quickly process data from the phone’s basic cameras and sensors. This allows for the swift detection of wildfires, often happening within minutes, sometimes even seconds, after ignition.
The system respects privacy by concentrating on areas with minimal human presence and mainly capturing images of greenery and wild areas. Advanced localization methods further ensure that it targets fire threats without mistakenly photographing people or buildings.
Living Sustainably with Extreme Climate Conditions
For individuals inhabiting areas adjacent to open landscapes filled with dry fuels like grass, shrubs, and timber, this rapid detection could be lifesaving, making the difference between keeping their homes or losing them.
In Southern California, this technology could lead the way in safeguarding residents and properties within wildland-urban interface (WUI) zones, such as the Hollywood Hills, Santa Monica Mountains, and San Gabriel Valley. Remarkably, the total cost for the entire setup is anticipated to be under $100, as highlighted by lead author and PhD student Xiao Fu.
“FireLoc envisions a future with enhanced wildfire responses, better assistance in WUI regions, and a more sustainable relationship with extreme climate conditions,” Fu remarked. “This is just the beginning of larger wildfire management strategies.”
The research was supported by a team including Barath Raghavan, Fu’s advisor and assistant professor of computer science, as well as Peter Bereel, a professor of electrical and computer engineering, along with students Yue Hu and Prashanth Sutrave.
Effective Testing in Wildfire Environments
Conventional wildfire detection methods, such as lookouts, satellites, and drones, present various challenges, including high costs, inconvenience, slow response times, and limited battery life. As a result, firefighters often rely on human observations to identify new fires, complicating the ability to accurately locate a blaze.
“This is especially overwhelming for fire departments, particularly during fast-moving wildfires like the one in Paradise,” noted Fu, referencing the tragic 2018 Camp Fire in Northern California that resulted in 85 deaths.
The team tested their mapping tool using a simulator modeled after data from the 2019 Getty Fire, where 745 acres were burned in Los Angeles. By conducting simulations within a realistic 3D model of the terrain and replicating authentic wildfire scenarios, they evaluated the tool’s efficiency, including its accuracy in localizing fires and scalability.
Cameras were set up to simulate the typical height of a two-story residential building, about 30 feet off the ground. The outcomes were indisputable: using FireLoc, the researchers detected over 40% of wildfires in the assessment area with only four cameras deployed.
“The simulator allows for thorough testing in wildfire conditions. We can manage scalability—like adding more cameras—and assess if accuracy and coverage will improve,” Fu explained.
Innovative Problem-Solving
While the data from the cameras is crucial, crowdsourcing also plays a key role. The setup only requires power, an internet connection, and the mobile phone secured in a weatherproof case to automatically capture images every 30 seconds or so.
“With several locations monitored, the system can determine the optimal spots for installing additional wildfire surveillance cameras,” Fu added.
When multiple cameras detect potential smoke or fire, they send information to a cloud server that integrates the images using digital elevation models, computer vision strategies, and other advanced computing methodologies. Describing this as a sophisticated yet vital process, Raghavan clarified that high-quality images aren’t necessary; algorithms help identify ideal camera placements for maximum coverage.
“We are amalgamating all visual data effectively to address the problem,” Raghavan stated. “This research not only reframes the challenge but also presents a viable solution for how to map fires as quickly as possible.”
To the researchers’ knowledge, this represents the first smart, low-cost crowdsourcing system tailored specifically for detecting wildfires.
In order to test the system in real-world settings, community members would need to affix smartphones to their properties to serve as wildfire detection sensors. The team intends to conduct future participatory studies to gauge public interaction with the technology. If implemented, would the researchers themselves participate?
For Fu—an outdoor lover who cherishes nature—it’s an obvious choice.
“Throughout my life, I’ve been active in green organizations and environmental initiatives,” Fu shared, recalling her upbringing on a family-owned fruit farm in Hainan, China. “Even while working indoors, I find joy in looking at images of trees and nature. I hope this innovation will help safeguard our natural landscapes amid the challenges posed by climate change.”
