论文标题
将计划情报纳入深度学习：街道网络设计的计划支持工具
Incorporating planning intelligence into deep learning: A planning support tool for street network design
论文作者
论文摘要
塑造临时计划建议的深度学习应用程序受到将有关城市与人工智能的专业知识相结合的困难的限制。我们建议对深度神经网络和计划指南进行新颖的互补使用，以使街道网络生成自动化，这可以是背景意识，示例和用户引导。该模型测试表明，在模型培训中纳入规划知识（例如，道路连接和邻里类型）会导致对街道配置的更现实的预测。此外，该新工具为专业用户提供了一个系统和直观地探索基准建议的机会，以进行比较和进一步的评估。
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.
