  After a hurricane, damage assessment is critical to emergency managers for
efficient response and resource allocation. One way to gauge the damage extent
is to quantify the number of flooded/damaged buildings, which is traditionally
done by ground survey. This process can be labor-intensive and time-consuming.
In this paper, we propose to improve the efficiency of building damage
assessment by applying image classification algorithms to post-hurricane
satellite imagery. At the known building coordinates (available from public
data), we extract square-sized images from the satellite imagery to create
training, validation, and test datasets. Each square-sized image contains a
building to be classified as either 'Flooded/Damaged' (labeled by volunteers in
a crowd-sourcing project) or 'Undamaged'. We design and train a convolutional
neural network from scratch and compare it with an existing neural network used
widely for common object classification. We demonstrate the promise of our
damage annotation model (over 97% accuracy) in the case study of building
damage assessment in the Greater Houston area affected by 2017 Hurricane
Harvey.
