Urban planning, which aims to design feasible land-use configurations for
target areas, has become increasingly essential due to the high-speed
urbanization process in the modern era. However, the traditional urban planning
conducted by human designers can be a complex and onerous task. Thanks to the
advancement of deep learning algorithms, researchers have started to develop
automated planning techniques. While these models have exhibited promising
results, they still grapple with a couple of unresolved limitations: 1)
Ignoring the relationship between urban functional zones and configurations and
failing to capture the relationship among different functional zones. 2) Less
interpretable and stable generation process. To overcome these limitations, we
propose a novel generative framework based on normalizing flows, namely
Dual-stage Urban Flows (DSUF) framework. Specifically, the first stage is to
utilize zone-level urban planning flows to generate urban functional zones
based on given surrounding contexts and human guidance. Then we employ an
Information Fusion Module to capture the relationship among functional zones
and fuse the information of different aspects. The second stage is to use
configuration-level urban planning flows to obtain land-use configurations
derived from fused information. We design several experiments to indicate that
our framework can outperform compared to other generative models for the urban
planning task.

智能化城市规划中的关键因素：自动化规划技术的双阶段城市流框架，通过信息融合模块将功能区之间的关系融合，以生成基于给定背景和人类指导的功能区，并进一步融合不同方面的信息，得到相应的土地利用配置。