  Study of urban form is an important area of research in urban planning/design
that contributes to our understanding of how cities function and evolve.
However, classical approaches are based on very limited observations and
inconsistent methods. As an alternative, availability of massive urban data
collections such as Open Street Map from the one hand and the recent
advancements in machine learning methods such as deep learning techniques on
the other have opened up new possibilities to automatically investigate urban
forms at the global scale. In this work for the first time, by collecting a
large data set of street networks in more than one million cities, towns and
villages all over the world, we trained a deep convolutional auto-encoder, that
automatically learns the hierarchical structures of urban forms and represents
them via dense and comparable vectors. We showed how the learned urban vectors
could 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 as
orientation, graphical structure, and density and partial deformations. Further
cluster analysis reveals the distribution of the main patterns of urban forms
all over the planet.
