用AI生成东京虚拟街区，量化城市身份特征。
Virtual Urbanism: An AI-Driven Framework for Quantifying Urban Identity. A Tokyo-Based Pilot Study Using Diffusion-Generated Synthetic Environments
- 用扩散模型生成无标识的东京合成街区，还原城市核心特征。
- 人类评估显示81%识别准确率，验证合成场景有效性。
- 提取文化嵌入的类型学元素，为智能城市分析提供新路径。
本文提出虚拟都市主义（Virtual Urbanism, VU）框架，通过合成城市环境量化城市身份。以东京九个区域为试点，采用Stable Diffusion与LoRA模型生成无导向标记的动态合成城市序列，旨在激发核心身份构成要素。人类评估实验（一）验证合成场景感知合法性；（二）量化区域层面的城市身份水平；（三）提取核心身份形成元素。结果表明，平均识别准确率达~81%，证实合成场景的有效性。提出的城市身份水平（UIL）指标可跨区域比较身份强度，语义分析揭示文化嵌入的类型学特征为核心身份要素。该框架为智能化、多参数城市身份度量提供了可行路径。
原文摘要 · Abstract (English)
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.
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