Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development

TL;DR

This paper presents a deep learning approach using street view imagery to classify urban street types, aiding modern urban planning by automating street labeling tasks.

Contribution

It introduces a method to collect, label, and classify street imagery with CNNs, improving efficiency and interpretability in urban street classification.

Findings

CNN models achieve 81-87% accuracy in street classification

t-SNE visualization reveals meaningful street embeddings

Class activation maps identify key visual features

Abstract

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.