The 'Paris-end' of town? Urban typology through machine learning

TL;DR

This paper introduces a neural network framework analyzing urban imagery to classify and compare city areas, revealing insights into urban design and its implications for health, transport, and environment.

Contribution

It presents a novel, image-based, neural network approach to objectively analyze and compare urban forms across thousands of cities worldwide.

Findings

Different imagery types highlight distinct urban features.

Map imagery emphasizes transportation and green spaces.

Street view imagery captures human-scale streetscape features.

Abstract

The confluence of recent advances in availability of geospatial information, computing power, and artificial intelligence offers new opportunities to understand how and where our cities differ or are alike. Departing from a traditional `top-down' analysis of urban design features, this project analyses millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics. A (novel) neural network-based framework is trained with imagery from the largest 1692 cities in the world and the resulting models are used to compare within-city locations from Melbourne and Sydney to determine the closest connections between these areas and their international comparators. This work demonstrates a new, consistent, and objective method to begin to understand the relationship between cities and their health, transport, and environmental consequences of their design. The results show specific advantages and disadvantages using each type of imagery. Neural networks trained with map imagery will be highly influenced by the mix of roads, public transport, and green and blue space as well as the structure of these elements. The colours of natural and built features stand out as dominant characteristics in satellite imagery. The use of street view imagery will emphasise the features of a human scaled visual geography of streetscapes. Finally, and perhaps most importantly, this research also answers the age-old question, ``Is there really a `Paris-end' to your city?''.