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The 'Paris-end' of town? Urban typology through machine learning | | Kerry A. Nice
; Jason Thompson
; Jasper S. Wijnands
; Gideon D.P.A. Aschwanden
; Mark Stevenson
; | | Date: |
8 Oct 2019 | | 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?’’. | | Source: | arXiv, 1910.3220 | | Services: | Forum | Review | PDF | Favorites | |
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