The 'Paris-end' of town? Urban typology through machine learning
This work provides a new, objective method for urban planners and researchers to analyze city design and its impacts on health, transport, and environment, though it is incremental in applying existing AI techniques to geospatial data.
The researchers tackled the problem of understanding urban typology by analyzing millions of images (street view, satellite, and street maps) from 1692 cities using a novel neural network framework, resulting in a method that identifies shared characteristics and compares locations in Melbourne and Sydney to international counterparts, showing specific advantages for each imagery type.
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?''.