  The classification of streets on road networks has been focused on the
vehicular transportational features of streets such as arterials, major roads,
minor roads and so forth based on their transportational use. City authorities
on the other hand have been shifting to more urban inclusive planning of
streets, encompassing the side use of a street combined with the
transportational features of a street. In such classification schemes, streets
are labeled for example as commercial throughway, residential neighborhood,
park etc. This modern approach to urban planning has been adopted by major
cities such as the city of San Francisco, the states of Florida and
Pennsylvania among many others. Currently, the process of labeling streets
according to their contexts is manual and hence is tedious and time consuming.
In this paper, we propose an approach to collect and label imagery data then
deploy advancements in computer vision towards modern urban planning. We
collect and label street imagery then train deep convolutional neural networks
(CNN) to perform the classification of street context. We show that CNN models
can perform well achieving accuracies in the 81% to 87%, we then visualize
samples from the embedding space of streets using the t-SNE method and apply
class activation mapping methods to interpret the features in street imagery
contributing to output classification from a model.
