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

arXiv: https://arxiv.org/abs/1911.08007 · HTML: https://mlsanity.com/paper/1911.08007/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Sensing accident-prone features in urban scenes for proactive driving and accident prevention](https://mlsanity.com/paper/2202.12788/) (2022)
- [ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science](https://mlsanity.com/paper/2412.18641/) (2024)
- [Predicting the impact of urban change in pedestrian and road safety](https://mlsanity.com/paper/2202.01781/) (2022)
- [Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes](https://mlsanity.com/paper/2607.15808/) (2026)

## Abstract (arXiv)

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.