Virtual Urbanism: An AI-Driven Framework for Quantifying Urban Identity. A Tokyo-Based Pilot Study Using Diffusion-Generated Synthetic Environments

TL;DR

This paper presents Virtual Urbanism, an AI-based framework that uses synthetic urban replicas to quantify and analyze urban identity, demonstrated through a Tokyo pilot study with promising accuracy and insights into cultural elements.

Contribution

Introduces a novel AI-driven framework for quantifying urban identity using synthetic environments, advancing automated, multi-parameter urban analysis methods.

Findings

Mean identification accuracy of ~81% for synthetic Tokyo areas

Urban Identity Level (UIL) metric effectively assesses identity across regions

Semantic analysis uncovers culturally embedded core identity elements

Abstract

This paper introduces Virtual Urbanism (VU), a multimodal AI-driven analytical framework for quantifying urban identity through the medium of synthetic urban replicas. The framework aims to advance computationally tractable urban identity metrics. To demonstrate feasibility, the pilot study Virtual Urbanism and Tokyo Microcosms is presented. A pipeline integrating Stable Diffusion and LoRA models was used to produce synthetic replicas of nine Tokyo areas rendered as dynamic synthetic urban sequences, excluding existing orientation markers to elicit core identity-forming elements. Human-evaluation experiments (I) assessed perceptual legitimacy of replicas; (II) quantified area-level identity; (III) derived core identity-forming elements. Results showed a mean identification accuracy of ~81%, confirming the validity of the replicas. Urban Identity Level (UIL) metric enabled assessment of identity levels across areas, while semantic analysis revealed culturally embedded typologies as core identity-forming elements, positioning VU as a viable framework for AI-augmented urban analysis, outlining a path toward automated, multi-parameter identity metrics.