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After a hurricane, drones can capture detailed images of damaged homes and roads. But getting those images to emergency responders is harder: The files are huge, and connectivity in the wake of a storm is often spotty. CLARKE, developed at Texas A&M University with funding from the National Science Foundation’s AI Institute for Societal Decision Making, is designed to speed up the process. After a drone lands, the CLARKE system can analyze images without an internet connection, and teams can then send compact reports detailing building damage and road conditions, rather than huge image files. The work is part of a broader effort to use AI in emergency response efforts. After Myanmar experienced a major earthquake in March 2025, Microsoft’s AI for Good Lab analyzed satellite imagery to help aid groups identify damaged buildings. By reducing delays in transferring and interpreting images, researchers estimate CLARKE could help emergency managers make decisions roughly a day earlier. This, in turn, allows them to focus on the hardest-hit areas first. In a serious crisis, that could save lives. “The information exists for emergency managers to make decisions, but it exists in a place where they can’t get to it,” Texas A&M researcher Tom Manzini said. Manzini says CLARKE can assess 2,000 homes in seven minutes. Emergency responders used the system after hurricanes Debby and Helene in 2024. It is now part of the standard operating procedure for Florida’s disaster drone team, FL-UAS1. In total, Manzini and his team have trained 125 emergency responders on using the CLARKE system.
document fetched 2026-10-29 03:03 · canonical https://semafor.com/article/10/26/2026/using-ai-to-map-disaster-damage-for-faster-rescue-and-recovery