AWS Blog: How EagleView Uses Deep Learning on AWS in Disaster Response
With data analytics becoming more sophisticated each day, the need for fast processing becomes more urgent. Shay Strong, Director of Data Science and Machine Learning at EagleView, talked to Amazon Web Services about how EagleView uses deep learning models on the AWS Cloud.
Natural disasters like the 2017 Santa Rosa fires and Hurricane Harvey cost hundreds of billions of dollars in property damages every year, wreaking economic havoc in the lives of homeowners. Insurance companies do their best to evaluate affected homes, but it could take weeks before assessments are available and salvaging and protecting the homes can begin. EagleView, a property data analytics company, is tackling this challenge with deep learning on AWS.
“Traditionally, the insurance companies would send out adjusters for property damage evaluation, but that could take several weeks because the area is flooded or otherwise not accessible,” explains Shay Strong, director of data science and machine learning at EagleView. “Using satellite, aerial, and drone images, EagleView runs deep learning models on the AWS Cloud to make accurate assessments of property damage within 24 hours. We provide this data to both large national insurance carriers and small regional carriers alike, to inform the homeowners and prepare next steps much more rapidly.”
