Incorporating planning intelligence into deep learning: A planning support tool for street network design
This work addresses the challenge for urban planners and lay users in automating street network generation, though it appears incremental as it combines existing deep learning with planning guidance.
The paper tackled the problem of integrating professional urban planning knowledge with deep learning for street network design, resulting in a tool that generates more realistic street configurations through context-aware, example-based, and user-guided methods.
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.