  Deep learning applications in shaping ad hoc planning proposals are limited
by the difficulty in integrating professional knowledge about cities with
artificial intelligence. We propose a novel, complementary use of deep neural
networks and planning guidance to automate street network generation that can
be context-aware, example-based and user-guided. The model tests suggest that
the incorporation of planning knowledge (e.g., road junctions and neighborhood
types) in the model training leads to a more realistic prediction of street
configurations. Furthermore, the new tool provides both professional and lay
users an opportunity to systematically and intuitively explore benchmark
proposals for comparisons and further evaluations.

Computer Vision and Pattern Recognition;Artificial Intelligence