  This study aims to enable more reliable automated post-disaster building
damage classification using artificial intelligence (AI) and multi-view
imagery. The current practices and research efforts in adopting AI for
post-disaster damage assessment are generally (a) qualitative, lacking refined
classification of building damage levels based on standard damage scales, and
(b) trained based on aerial or satellite imagery with limited views, which,
although indicative, are not completely descriptive of the damage scale. To
enable more accurate and reliable automated quantification of damage levels,
the present study proposes the use of more comprehensive visual data in the
form of multiple ground and aerial views of the buildings. To have such a
spatially-aware damage prediction model, a Multi-view Convolution Neural
Network (MV-CNN) architecture is used that combines the information from
different views of a damaged building. This spatial 3D context damage
information will result in more accurate identification of damages and reliable
quantification of damage levels. The proposed model is trained and validated on
reconnaissance visual dataset containing expert-labeled, geotagged images of
the inspected buildings following hurricane Harvey. The developed model
demonstrates reasonably good accuracy in predicting the damage levels and can
be used to support more informed and reliable AI-assisted disaster management
practices.
