Imaging Science MS Thesis Defense - Salman Khan
The Use of Deep Learning Methods to Assist in the Classification of Seismically Vulnerable Dwellings
Imaging Science MS Candidate
Chester F. Carlson Center for Imaging Science, RIT
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Exciting research is being conducted using Google's street view imagery. Researchers can have access to training data that allows CNN training for topics ranging from assessing neighborhood environments to estimating the age of a building. However, due to the uncontrolled nature of imagery available via Google's Street View API, data collection can be lengthy and tedious. In an effort to help researchers gather address specific dwelling images efficiently, we developed an innovative and novel way of automatically performing this task. It was accomplished by exploiting Google's publicly available platform with a combination of 3 separate network types and post-processing techniques. Our uniquely developed NMS strategy helped achieve 99.4%, valid, address specific, dwelling images. We explored the efficacy of utilizing our newly developed mechanism to train a CNN on Unreinforced Masonry (URM) buildings. This is because, building collapse during an earthquake account for majority of the deaths during a natural disaster of this kind. An automated approach for identifying seismically vulnerable buildings using street level imagery has been met with limited success to this point with no promising results presented in the literature. We have been able to achieve the best accuracy reported to date, at 83.63%, in identifying URM, finished URM, and non-URM buildings using manually curated images. We performed an ablation study to establish synergistic parameters on ResNeXt-101-FixRes. Lastly, we present a visualization the first layer of the network to ascertain and demonstrate how a deep learning network can distinguish between various types of URM buildings.
Undergraduates, graduates, and experts. Those with interest in the topic.
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