Wildfire events are increasingly devastating communities across the United States, presenting a significant challenge for the insurance industry in accurately assessing and managing property risks. This project proposes the development of a machine learning and computer vision-based solution that leverages aerial and satellite imagery to evaluate wildfire risks at the property level. The proposed solution will identify defensible space and classify vegetation around properties, offering a comprehensive, scalable solution for wildfire risk assessment that can inform both insurance strategies and homeowner mitigation efforts.
Keywords: computer vision, machine learning, wildfires, insurance industry, image segmentation, risk
Watch the team present this project in the session recording here.
Faculty Advisor
Ashish Pujari is a Data and AI consultant, practitioner, and educator with over 20 years of experience in in machine learning, big data, and cloud computing. He has led large global technology and data science teams and consulted for Fortune 500 companies in banking, finance, healthcare, insurance, and manufacturing.
As a Principal ML/AI Architect at AWS, Ashish provides strategic guidance to enterprise customers on leveraging the cloud for AI and Machine Learning. Prior to joining Amazon, he served in various technology leadership roles at Credera, GLG, IRI, and Pegasystems. Ashish holds a Master of Science in Analytics from the University of Chicago and BS in Electrical Engineering from the National Institute of Technology, Rourkela.