Web: http://arxiv.org/abs/2201.10389

Jan. 26, 2022, 2:11 a.m. | Ali Ismail, Mariette Awad

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv building change cv detection framework graph networks

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