  In the aftermath of disasters, building damage maps are obtained using change
detection to plan rescue operations. Current convolutional neural network
approaches do not consider the similarities between neighboring buildings for
predicting the damage. We present a novel graph-based building damage detection
solution to capture these relationships. Our proposed model architecture learns
from both local and neighborhood features to predict building damage.
Specifically, we adopt the sample and aggregate graph convolution strategy to
learn aggregation functions that generalize to unseen graphs which is essential
for alleviating the time needed to obtain predictions for new disasters. Our
experiments on the xBD dataset and comparisons with a classical convolutional
neural network reveal that while our approach is handicapped by class
imbalance, it presents a promising and distinct advantage when it comes to
cross-disaster generalization.
