  Humanitarian disasters and political violence cause significant damage to our
living space. The reparation cost to homes, infrastructure, and the ecosystem
is often difficult to quantify in real-time. Real-time quantification is
critical to both informing relief operations, but also planning ahead for
rebuilding. Here, we use satellite images before and after major crisis around
the world to train a robust baseline Residual Network (ResNet) and a disaster
quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness
to poor image quality and can identify areas of destruction with high accuracy
(92\%), whereas PSPNet offers contextualised quantification of built
environment damage with good accuracy (84\%). As there are multiple damage
dimensions to consider (e.g. economic loss and fatalities), we fit a
multi-linear regression model to quantify the overall damage. To validate our
combined system of deep learning and regression modeling, we successfully match
our prediction to the ongoing recovery in the 2020 Beirut port explosion. These
innovations provide a better quantification of overall disaster magnitude and
inform intelligent humanitarian systems of unfolding disasters.
