Extreme weather events such as hurricanes and floods cause increasing damage to communities, leading to substantial economic losses and displacement of populations1–6. Previous research suggests that there are disparities in the resilience capacity of neighbourhoods, predicting a recovery mechanism of either segmented withdrawal or reinforcement across different neighbourhood groups7–12. Assessing these hypotheses and investigating if—and to what extent—neighbourhood built environments recover at scale has been difficult because previous measures have relied on aggregated survey data1,7,9–14. Here we construct a building-level disaster recovery dataset covering 2,195 census tracts spanning 16 states and across 12 extreme weather events in the USA from 2007 to 2023 using historical street view imagery and multimodal machine learning. Our analysis shows that in the aftermath of extreme weather events, lower-income neighbourhoods are less likely to rebuild and do not return to their pre-disaster state, whereas higher-income areas rebuild and tend to improve compared with their pre-disaster state, highlighting increasing disparities in their built environments. We further investigate those disparities by examining the deployment of disaster recovery assistance and insurance policies, and identify a resource gap for lower-income neighbourhoods that may explain unequal community responses to extreme weather events. Our findings demonstrate the value of analysing neighbourhood recovery trajectories at a higher resolution and larger scale to inform responsive policy designs, and suggest the importance of restructuring the recovery financial assistance framework to promote more climate resilient communities.
