Grapevine

Eyeing the damage of hurricane season

In the aftermath of hurricanes like Helene and Milton, the damaging effects of these natural disasters are the center of national conversations, including questions about the long-term impact to infrastructure. However, current methods for damage assessment don't offer clear and timely answers to these questions.

That's where AI and engineering can help. Researchers from Texas A&M University are pioneering the use of AI and machine learning to create faster methods to assess damages caused by hurricanes.

Dr. Robin Murphy and her research team, led by computer science and engineering Ph.D. student Tom Manzini, have spent over a year working to create an open-source dataset, known as CRASAR-U-DROIDS. This dataset is the world's largest set of annotated imagery obtained from drones, which were flown over 10 disasters, including Hurricanes Harvey, Michael, Ida, Laura, Ian, and Idalia.

With the help from 130 high school students in Texas and Pennsylvania, the team labeled the level of damage for 14,000 buildings on 8,000 acres of land and 680 miles of roads.

Using this dataset, Manzini and fellow graduate student Priya Perali trained an AI system to recognize building and road damage caused by disasters. Learning these models took hours of high-performance computing but have resulted in a damage assessment system that can sort through the building and road damages of a large neighborhood after a disaster in only four minutes using a laptop.