  Near-real time estimation of damage to buildings and infrastructure, referred
to as damage nowcasting in this study, is crucial for empowering emergency
responders to make informed decisions regarding evacuation orders and
infrastructure repair priorities during disaster response and recovery. Here,
we introduce FloodDamageCast, a machine learning framework tailored for
property flood damage nowcasting. The framework leverages heterogeneous data to
predict residential flood damage at a resolution of 500 meters by 500 meters
within Harris County, Texas, during the 2017 Hurricane Harvey. To deal with
data imbalance, FloodDamageCast incorporates a generative adversarial
networks-based data augmentation coupled with an efficient machine learning
model. The results demonstrate the model's ability to identify high-damage
spatial areas that would be overlooked by baseline models. Insights gleaned
from flood damage nowcasting can assist emergency responders to more
efficiently identify repair needs, allocate resources, and streamline
on-the-ground inspections, thereby saving both time and effort.
