SAN LUIS OBISPO –– A pair of Cal Poly professors and a team of students have used artificial intelligence to train a computer to quickly assess wildfire damage -- potentially improving response time for efforts to recover from major wildfires. Accurate and timely damage assessment has become critical for response and recovery as the threat of wildfires increases. Damage assessment reports inform first responders' strategies, affect residents' ability to file insurance claims, and guide state and federal authorities' plans for future disaster relief and financial aid. To date, most wildfire event inspectors must personally visit affected areas and manually document the severity of building damage, a process that often takes weeks. Social sciences Assistant Professor Andrew Fricker, computer science Assistant Professor Jonathan Ventura, visiting Cal Poly undergraduate student Gustave Rousselet, and a team of Stanford doctoral students sought to streamline this process with artificial intelligence (AI) deep learning.

People around the globe have suffered the nerve-wracking anxiety of waiting weeks or months to find out if their homes have been damaged by wildfires that scorch with increased intensity. Now, once the smoke has cleared for aerial photography, researchers have found a way to identify building damage within minutes. Through a system they call DamageMap, a team at Stanford University and the California Polytechnic State University (Cal Poly) has brought an artificial intelligence approach to building assessment: Instead of comparing before-and-after photos, they've trained a program using machine learning to rely solely on post-fire images. The findings appear in the International Journal of Disaster Risk Reduction. "We wanted to automate the process and make it much faster for first responders or even for citizens that might want to know what happened to their house after a wildfire," said lead study author Marios Galanis, a graduate student in the Civil and Environmental Engineering Department at Stanford's School of Engineering.