CSTARS’ Algorithm for Early Detection of California Wildfires Featured in Data Makes Possible

04.02.19

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To combat future devastation, scientists across the public and private sectors are turning to data-driven technologies to better detect wildfires before they spread. Machine learning is one technology proving critical in providing actionable insights to help first responders identify wildfires early and come up with informed strategies to combat the tens of thousands of wildfires4that occur in the U.S. — and burn increasingly more acreage— each year.

Monitoring Wildfires from Space

One of the most critical aspects of curbing a wildfire’s devastation is early detection. Some areas are equipped with lookout towers and surveillance cameras to aide in this, but first responders today rely primarily on calls to emergency services to identify wildfires. And when fires ignite in remote areas, as the Camp Fire did, that method falls short.

CSTARS scientist Alex Koltunov
Alex Koltunov, CSTARS scientist. (Photo courtesy of CSTARS)

One organization working to detect wildfire ignitions in near real-time6with the help of artificial intelligence (specifically, machine learning) is the Center for Spatial Technologies and Remote Sensing (CSTARS) at the University of California, Davis.

machine learning joins wildfire fight in california
GOES-16 Satellite Image of the Camp Fire (Photo Courtesy of CSTARS)

Alex Koltunov, a CSTARS scientist, is co-leading the effort. Koltunov and his team have developed an algorithm that conducts image analysis on data from the GOES weather satelliteswhich scans approximately 25-square-kilometer areas in California (called pixels) about every 15 minutes. The algorithm, called GOES-Early Fire Detection7or GOES-EFD, must be able to perceive incredibly subtle environmental abnormalities to detect wildfires early, such as temperature changes, while considering other factors that could affect temperature like fog, clouds or wind. To accomplish this, the team developed a detection model that uses machine learning to compare a pixel’s current conditions to past and current data from similar pixels under normal conditions. If the model detects a wildfire, it can send alerts to first responders. In early experiments, Koltunov has been able to detect wildfires before they were reported by conventional methods. Moreover, the GOES-178satellite — deployed on February 12, 2019 — has pixels that are four times smaller and scans at five-minute intervals, meaning the CSTARS algorithm could help make early detection routine.