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.

翻译：本文提出虚拟都市主义（VU），一种通过合成城市复现体来量化城市身份的多模态人工智能驱动分析框架。该框架旨在推进计算可处理的城市身份度量指标。为验证可行性，本研究展示了试点项目“虚拟都市主义与东京微观世界”。通过整合Stable Diffusion与LoRA模型的流程，生成了九个东京区域的合成复现体，并将其呈现为动态合成城市序列，同时排除现有导向标识以激发核心身份构成要素。人类评估实验（I）检验了复现体的感知可信度；（II）量化了区域层面的身份特征；（III）提取了核心身份构成要素。结果显示平均识别准确率约达81%，证实了复现体的有效性。城市身份水平（UIL）指标实现了跨区域身份水平的评估，而语义分析则揭示了文化嵌入的类型学作为核心身份构成要素。这些发现确立了VU作为人工智能增强城市分析可行框架的地位，并为实现自动化多参数身份度量指明了路径。