  In countries experiencing unprecedented waves of urbanization, there is a
need for rapid and high quality urban street design. Our study presents a novel
deep learning powered approach, DeepStreet (DS), for automatic street network
generation that can be applied to the urban street design with local
characteristics. DS is driven by a Convolutional Neural Network (CNN) that
enables the interpolation of streets based on the areas of immediate vicinity.
Specifically, the CNN is firstly trained to detect, recognize and capture the
local features as well as the patterns of the existing street network sourced
from the OpenStreetMap. With the trained CNN, DS is able to predict street
networks' future expansion patterns within the predefined region conditioned on
its surrounding street networks. To test the performance of DS, we apply it to
an area in and around the Eixample area in the City of Barcelona, a well known
example in the fields of urban and transport planning with iconic grid like
street networks in the centre and irregular road alignments farther afield. The
results show that DS can (1) detect and self cluster different types of complex
street patterns in Barcelona; (2) predict both gridiron and irregular street
and road networks. DS proves to have a great potential as a novel tool for
designers to efficiently design the urban street network that well maintains
the consistency across the existing and newly generated urban street network.
Furthermore, the generated networks can serve as a benchmark to guide the local
plan-making especially in rapidly developing cities.
