AI & Digital Twins for Infrastructure

We develop AI and vision-language models for rapid infrastructure damage assessment from post-disaster imagery. Our research focuses on recognizing damage severity across different disasters and damage classification systems, while addressing the challenges of limited labeled data in new disaster scenarios.

Current work explores prompt-guided learning, cross-domain transfer learning, and few-shot adaptation to improve the scalability and reliability of post-disaster damage assessment.