  In post-event reconnaissance missions, engineers and researchers collect
perishable information about damaged buildings in the affected geographical
region to learn from the consequences of the event. A typical post-event
reconnaissance mission is conducted by first doing a preliminary survey,
followed by a detailed survey. The preliminary survey is typically conducted by
driving slowly along a pre-determined route, observing the damage, and noting
where further detailed data should be collected. This involves several manual,
time-consuming steps that can be accelerated by exploiting recent advances in
computer vision and artificial intelligence. The objective of this work is to
develop and validate an automated technique to support post-event
reconnaissance teams in the rapid collection of reliable and sufficiently
comprehensive data, for planning the detailed survey. The technique
incorporates several methods designed to automate the process of categorizing
buildings based on their key physical attributes, and rapidly assessing their
post-event structural condition. It is divided into pre-event and post-event
streams, each intending to first extract all possible information about the
target buildings using both pre-event and post-event images. Algorithms based
on convolutional neural network (CNNs) are implemented for scene (image)
classification. A probabilistic approach is developed to fuse the results
obtained from analyzing several images to yield a robust decision regarding the
attributes and condition of a target building. We validate the technique using
post-event images captured during reconnaissance missions that took place after
hurricanes Harvey and Irma. The validation data were collected by a structural
wind and coastal engineering reconnaissance team, the National Science
Foundation (NSF) funded Structural Extreme Events Reconnaissance (StEER)
Network.
