Disaster damage assessment in the U.S. is increasingly important as natural hazard-induced disasters (e.g., hurricanes) are breaking records nearly every year, costing the nation hundreds of billions of dollars per year. However, the current practice of disaster damage assessment is largely dependent on humans (e.g., ground surveys) being slow, costly and error-prone.
This project seeks to improve current practices for damage assessment by using a computer vision algorithm to automatically annotate remotely sensed imagery captured after Hurricane Harvey in Houston, Texas. The presenter is Youngjun Choe, Ph.D., an assistant professor of Industrial & Systems Engineering at the University of Washington, Seattle.
Learn more at:Damage Assessment on Post-Hurricane Imagery
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