  During natural disasters, aircraft and satellites are used to survey the
impacted regions. Usually human experts are needed to manually label the
degrees of the building damage so that proper humanitarian assistance and
disaster response (HADR) can be achieved, which is labor-intensive and
time-consuming. Expecting human labeling of major disasters over a wide area
gravely slows down the HADR efforts. It is thus of crucial interest to take
advantage of the cutting-edge Artificial Intelligence and Machine Learning
techniques to speed up the natural infrastructure damage assessment process to
achieve effective HADR. Accordingly, the paper demonstrates a systematic effort
to achieve efficient building damage classification. First, two novel
generative adversarial nets (GANs) are designed to augment data used to train
the deep-learning-based classifier. Second, a contrastive learning based method
using novel data structures is developed to achieve great performance. Third,
by using information fusion, the classifier is effectively trained with very
few training data samples for transfer learning. All the classifiers are small
enough to be loaded in a smart phone or simple laptop for first responders.
Based on the available overhead imagery dataset, results demonstrate data and
computational efficiency with 10% of the collected data combined with a GAN
reducing the time of computation from roughly half a day to about 1 hour with
roughly similar classification performances.
