  Streets networks provide an invaluable source of information about the
different temporal and spatial patterns emerging in our cities. These streets
are often represented as graphs where intersections are modelled as nodes and
streets as links between them. Previous work has shown that raster
representations of the original data can be created through a learning
algorithm on low-dimensional representations of the street networks. In
contrast, models that capture high-level urban network metrics can be trained
through convolutional neural networks. However, the detailed topological data
is lost through the rasterisation of the street network. The models cannot
recover this information from the image alone, failing to capture complex
street network features. This paper proposes a model capable of inferring good
representations directly from the street network. Specifically, we use a
variational autoencoder with graph convolutional layers and a decoder that
outputs a probabilistic fully-connected graph to learn latent representations
that encode both local network structure and the spatial distribution of nodes.
We train the model on thousands of street network segments and use the learnt
representations to generate synthetic street configurations. Finally, we
proposed a possible application to classify the urban morphology of different
network segments by investigating their common characteristics in the learnt
space.
