城市社区的空间规划通过深度强化学习
Yu Zheng1,2, Yuming Lin1,2, Liang Zhao3
1Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, P. R. China.
Nature computational science
|January 4, 2024
概括
人工智能 (AI) 正在通过开发一种用于生成高效空间规划的新型模型来彻底改变城市规划. 这种人工智能方法的表现优于人类专家,增强了可持续城市发展.
科学领域:
- 城市科学 城市科学
- 人工智能的人工智能
- 计算地理学的计算地理学
背景情况:
- 有效的空间规划对于可持续的城市发展至关重要.
- 当前的城市规划在很大程度上依赖于人类专家,尽管有GIS和CAD的进步.
- 多样和不规则的城市地理对传统的规划方法提出了挑战.
研究的目的:
- 提出一个人工智能 (AI) 城市规划模型,用于生成空间规划.
- 解决城市地理的复杂性和规划中的巨大解决方案空间.
- 引入人类-人工智能协作工作流程,以改善城市设计.
主要方法:
- 构建图形来表示任意城市形式的城市拓.
- 在图表上,将城市规划作为一个连续的决策问题.
- 开发基于图形神经网络 (GNN) 的强化学习模型.
主要成果:
- 人工智能模型在城市规划的客观指标上明显优于人类专家.
- 该模型生成可适应的空间规划,以应对各种情况和需求.
- 在合成和现实数据上的实验验验证了模型的有效性.
结论:
- 计算式城市规划在应对复杂的城市挑战方面具有重大潜力.
- 拟议的AI模型为创建高效和可持续的城市空间规划提供了一个强大的工具.
- 人与人工智能的协作工作流可以提高设计师的生产力和计划效率.
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