Towards Cross-Disaster Building Damage Detection with Graph Convolutional Networks

In the aftermath of disasters, building damage maps are ob-tained using change detection to plan rescue operations. Cur-rent convolutional neural network approaches do not consider the similarities between neighboring buildings for predicting the damage. We present a novel graph-based building dam-age detection solution to capture these relationships. Our pro-posed model architecture learns from both local and neigh-borhood 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 neu-ral network reveal that while our approach is handicapped by class imbalance, it presents a promising and distinct advan-tage when it comes to cross-disaster generalization.