  Change detection is instrumental to localize damage and understand
destruction in disaster informatics. While convolutional neural networks are at
the core of recent change detection solutions, we present in this work, BLDNet,
a novel graph formulation for building damage change detection and enable
learning relationships and representations from both local patterns and
non-stationary neighborhoods. More specifically, we use graph convolutional
networks to efficiently learn these features in a semi-supervised framework
with few annotated data. Additionally, BLDNet formulation allows for the
injection of additional contextual building meta-features. We train and
benchmark on the xBD dataset to validate the effectiveness of our approach. We
also demonstrate on urban data from the 2020 Beirut Port Explosion that
performance is improved by incorporating domain knowledge building
meta-features.
