Generative AI for disaster simulation: Pre-enacting urban multi-hazard scenarios and enhancing climate-adaptive resilience with street view data.

Urban disaster simulation plays a crucial role in facilitating proactive mitigation strategies and enhances urban resilience. However, intuitive and precise methods for visualizing and pre-enacting prospective disaster scenarios have not received sufficient attention. This study proposes a novel framework that leverages generative artificial intelligence (AI) to pre-enact and evaluate urban scenarios under multiple extreme weather events across 20 climate-vulnerable cities in China. The framework integrates multimodal generative AI models with a large dataset comprising 1620 disaster photographs (2010-2024) and 528 street-view images. Through a workflow that includes disaster scene generation, semantic recognition of street elements, resilience risk assessment, and environmental intervention strategy generation, the proposed approach derives precisely targeted climate adaptation strategies. The semantic similarity between generated disaster scenes and real-world scenes exceeds 0.82. The results indicate that increasing green coverage, improving sidewalk accessibility, enhancing traffic management, and upgrading public service facilities are key measures for improving multi-hazard resilience. Furthermore, the study proposes differentiated resilience strategy packages tailored to three types of disaster scenarios: weather response-oriented, temperature response-oriented, and geological response-oriented scenarios. These findings demonstrate the potential of generative AI tools to support evidence-based urban resilience policy formulation.