Research Repository

Street-Frontage-Net: urban image classification using deep convolutional neural networks

Law, Stephen and Seresinhe, Chanuki Illushka and Shen, Yao and Gutierrez-Roig, Mario (2020) 'Street-Frontage-Net: urban image classification using deep convolutional neural networks.' International Journal of Geographical Information Science, 34 (4). 681 - 707. ISSN 1365-8816

stree-frontage-net.pdf - Accepted Version

Download (6MB) | Preview


Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design.

Item Type: Article
Uncontrolled Keywords: Urban design, deep learning, convolutional neural network, machine vision, Google Street View
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Elements
Date Deposited: 07 Sep 2020 14:32
Last Modified: 07 Sep 2020 14:32

Actions (login required)

View Item View Item