| Summary: | The domains relevant to urban planning have been disrupted by the proliferation of highly
granular city data and the advancements in machine learning. However, machine learning models are susceptible to pitfalls constraining their deployment in many applications
including domains related to urban settings. There is much to be addressed between the
methods and applications before we can realize all potentials of machine learning to improve urban life. In this thesis, we focus on the use of streets imagery and classification
problems. We start motivating the thesis with a case study where deep learning models
are trained to predict street contexts (i.e. residential, park, commercial...etc) from streets
imagery. We then shift gears and discuss a novel unsupervised domain adaptation method
to address the drop in accuracy when models are tested outside the domain of the training
data (i.e. a model trained on San Francisco and tested in Boston). We further our discussion with a proof of concept of a framework to develop more generalized models starting
with a prototype of a system of streets imagery collection, labeling, and ending with how
we approach generalization by breaking the problem into smaller prediction tasks to aid in
more understanding of the interworking of the models.
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