  In the aftermath of an earthquake, rapid structural inspections are required
to get citizens back in to their homes and offices in a safe and timely manner.
These inspections gfare typically conducted by municipal authorities through
structural engineer volunteers. As manual inspec-tions can be time consuming,
laborious and dangerous, research has been underway to develop methods to help
speed up and increase the automation of the entire process. Researchers
typi-cally envisage the use of unmanned aerial vehicles (UAV) for data
acquisition and computer vision for data processing to extract actionable
information. In this work we propose a new framework to generate vision-based
condition-aware models that can serve as the basis for speeding up or
automating higher level inspection decisions. The condition-aware models are
generated by projecting the inference of trained deep-learning models on a set
of images of a structure onto a 3D mesh model generated through multi-view
stereo from the same image set. Deep fully convolutional residual networks are
used for semantic segmentation of images of buildings to provide (i) damage
information such as cracks and spalling (ii) contextual infor-mation such as
the presence of a building and visually identifiable components like windows
and doors. The proposed methodology was implemented on a damaged building that
was sur-veyed by the authors after the Central Mexico Earthquake in September
2017 and qualitative-ly evaluated. Results demonstrate the promise of the
proposed method towards the ultimate goal of rapid and automated
post-earthquake inspections.
