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Study of urban form is an important area of research in urban planning/designthat contributes to our understanding of how cities function and evolve.However, classical approaches are based on very limited observations andinconsistent methods. As an alternative, availability of massive urban datacollections such as Open Street Map from the one hand and the recentadvancements in machine learning methods such as deep learning techniques onthe other have opened up new possibilities to automatically investigate urbanforms at the global scale. In this work for the first time, by collecting alarge data set of street networks in more than one million cities, towns andvillages all over the world, we trained a deep convolutional auto-encoder, thatautomatically learns the hierarchical structures of urban forms and representsthem via dense and comparable vectors. We showed how the learned urban vectorscould be used for different investigations. Using the learned urban vectors,one is able to easily find and compare similar urban forms all over the world,considering their overall spatial structure and other factors such asorientation, graphical structure, and density and partial deformations. Furthercluster analysis reveals the distribution of the main patterns of urban formsall over the planet.

Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet