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.
@article{arxiv.2010.04536,
title = {Incorporating planning intelligence into deep learning: A planning support tool for street network design},
author = {Zhou Fang and Ying Jin and Tianren Yang},
journal= {arXiv preprint arXiv:2010.04536},
year = {2020}
}