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

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High-resolution resilience assessment identifies vulnerable buildings in urban settings, with implications for disaster response.
Analysis includes resilience metrics from 30,590 structures in a major earthquake-prone region of Türkiye.
The study employs machine learning and statistical methods for assessing disaster resilience at the building level.
Resource allocation support may enable better planning and targeted interventions to improve urban disaster resilience.

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Observational analysis assesses resilience at building level in Elazig, indicating high-resolution systems may enhance disaster resilience efforts.

Kaya et al. (2025) studied this question.

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Observational analysis assesses resilience at building level in Elazig, indicating high-resolution systems may enhance disaster resilience efforts.