Downscaling Urban Disaster Resilience: Toward Building‐Scale Geo‐Information Modeling and Machine Learning‐Assisted Resilience Knowledge Systems

ABSTRACT Cities bear the brunt of damages brought by extreme events in terms of infrastructure disruptions, societal impacts, and manifold long‐lasting consequences—calling for resilience building. Current resilience comprehension at higher resolution (i.e., building level) is limited, which compromises risk planning efforts. We here propose a high‐resolution resilience assessment, profiling, and knowledge system building framework at the building/block scale for targeted interventions. The framework's contribution to practice is presented by assessing resilience at the building level ( n = 30,590) in one of the most earthquake‐hit regions in Türkiye, namely Elazig. Leveraging geo‐information modeling, machine learning, statistical methods, and data on 40 representative indicators, we compute and map resilience from a systems‐of‐system perspective . While a significant geographical disparity in disaster resilience (overall as well as subdomain resilience) is evident, ~46% of buildings are identified as the least resilient (95% confidence). On a subdomain level, ~50% of buildings are identified in a worst socio‐economic situation (i.e., the bottom 20% of the overall performance distribution). An online interactive dashboard is developed for spatial distributions and key analytics regarding building‐level resilience intelligence—acting as a baseline resilience knowledge system for decisions and resource allocation support. Overall, this study offers a means and guide to establish national‐scale high‐resolution resilience knowledge systems in cities supporting decision‐makers and concerned local authorities to strategize actionable tactics for future vigilance to reduce the deteriorating impacts of disasters in cities.