RECITYGEN -- Interactive and Generative Participatory Urban Design Tool with Latent Diffusion and Segment Anything

2026-02-04

AI Summary

This study addresses the frequent neglect of public participation in conventional top-down urban design, which often results in proposals misaligned with actual community needs. To bridge this gap, the authors propose an interactive semantic segmentation framework that integrates a latent diffusion model with Segment Anything, enabling, for the first time, the generation of diverse and editable street-view images through natural language prompts. This approach empowers residents to directly engage in the visual design of urban renewal projects. Pilot implementation in Beijing successfully captured local preferences, demonstrating the tool’s feasibility and innovative potential in fostering dynamic, inclusive urban planning processes.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Urban design profoundly impacts public spaces and community engagement. Traditional top-down methods often overlook public input, creating a gap in design aspirations and reality. Recent advancements in digital tools, like City Information Modelling and augmented reality, have enabled a more participatory process involving more stakeholders in urban design. Further, deep learning and latent diffusion models have lowered barriers for design generation, providing even more opportunities for participatory urban design. Combining state-of-the-art latent diffusion models with interactive semantic segmentation, we propose RECITYGEN, a novel tool that allows users to interactively create variational street view images of urban environments using text prompts. In a pilot project in Beijing, users employed RECITYGEN to suggest improvements for an ongoing Urban Regeneration project. Despite some limitations, RECITYGEN has shown significant potential in aligning with public preferences, indicating a shift towards more dynamic and inclusive urban planning methods. The source code for the project can be found at RECITYGEN GitHub.

Problem

• participatory urban design
• public input
• urban regeneration
• design gap
• community engagement

Innovation

• Latent Diffusion Models
• Segment Anything
• Participatory Urban Design
• Interactive Generative Tool
• Semantic Segmentation