  The machine learning community has recently had increased interest in the
climate and disaster damage domain due to a marked increased occurrences of
natural hazards (e.g., hurricanes, forest fires, floods, earthquakes). However,
not enough attention has been devoted to mitigating probable destruction from
impending natural hazards. We explore this crucial space by predicting
building-level damages on a before-the-fact basis that would allow state actors
and non-governmental organizations to be best equipped with resource
distribution to minimize or preempt losses. We introduce PreDisM that employs
an ensemble of ResNets and fully connected layers over decision trees to
capture image-level and meta-level information to accurately estimate weakness
of man-made structures to disaster-occurrences. Our model performs well and is
responsive to tuning across types of disasters and highlights the space of
preemptive hazard damage modelling.
