Image Generation Techniques for Urban Planning

2026-08-19

AI Summary

本文开发了一种基于掩码加权条件流匹配的生成AI模型，以自动创建符合城市规划需求的建筑展示图像，并通过特定风格指南优化模型。

Abstract

In the context of urban planning, architects are normally instructed with creating presentation images that visualize proposed buildings within their urban context. This work aims to develop a GenAI model for automatically generating architectural presentation images in urban scenes, with emphasis on model optimization. To achieve this, we developed Mask-based Weighted Conditional Flow Matching (MWCFM), which extends Flow Matching by introducing contextual masks for precise feature focusing. This enables targeted training on critical spatial elements relevant to urban planning. Our trained model learns from urban street-view data while adhering to specific style-guidelines, which are integrated into training through the loss function. Furthermore, the model's performance is evaluated using application related metrics, derived from presentation image style guidelines.

Problem

• Urban Planning
• Architectural Presentation Images
• GenAI Model
• Contextual Masks
• Style-Guidelines

Innovation

• Mask-based Weighted Conditional Flow Matching
• contextual masks
• feature focusing
• urban planning
• style-guidelines