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In post-event reconnaissance missions, engineers and researchers collectperishable information about damaged buildings in the affected geographicalregion to learn from the consequences of the event. A typical post-eventreconnaissance mission is conducted by first doing a preliminary survey,followed by a detailed survey. The preliminary survey is typically conducted bydriving slowly along a pre-determined route, observing the damage, and notingwhere further detailed data should be collected. This involves several manual,time-consuming steps that can be accelerated by exploiting recent advances incomputer vision and artificial intelligence. The objective of this work is todevelop and validate an automated technique to support post-eventreconnaissance teams in the rapid collection of reliable and sufficientlycomprehensive data, for planning the detailed survey. The techniqueincorporates several methods designed to automate the process of categorizingbuildings based on their key physical attributes, and rapidly assessing theirpost-event structural condition. It is divided into pre-event and post-eventstreams, each intending to first extract all possible information about thetarget buildings using both pre-event and post-event images. Algorithms basedon convolutional neural network (CNNs) are implemented for scene (image)classification. A probabilistic approach is developed to fuse the resultsobtained from analyzing several images to yield a robust decision regarding theattributes and condition of a target building. We validate the technique usingpost-event images captured during reconnaissance missions that took place afterhurricanes Harvey and Irma. The validation data were collected by a structuralwind and coastal engineering reconnaissance team, the National ScienceFoundation (NSF) funded Structural Extreme Events Reconnaissance (StEER)Network.

Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion