大语言模型（LLMs）正日益被用于描述、评估和解读地点，但其是否基于文化中立立场开展此类任务仍不明确。本研究利用一个平衡的全球街景样本，并采用中性提示或唤起不同区域文化立场的提示，在前沿LLM中检验城市感知能力。在开放式描述与结构化地点判断两类任务中，“中性”条件在实践中并未表现出中立性：与欧洲及北美相关的提示始终系统性地更接近模型基线，而许多非西方提示则偏离较远，表明模型感知并非围绕普适性参考框架组织，而是围绕一种文化上不均衡的参考框架组织。文化提示亦会改变情感评价，对某些被提示的身份产生基于情感的内群体偏好。与区域性人类图文基准的对比显示，文化邻近性提示可提升模型描述与人类描述的一致性，但无法恢复人类所具有的语义多样性水平，且往往保留情感基调偏高的表达风格。同样的不对称性亦出现在关于安全性、美观度、富裕程度、活力、乏味感与抑郁感的结构化判断中：模型输出虽可解释，却仅部分复现了人类群体间的差异。这些发现表明，LLMs并非从“无地点”（nowhere）出发感知城市；它们是通过一种文化上不均衡的基线进行感知，该基线塑造了何者被视为寻常、熟悉及具有正面价值。

Large language models (LLMs) are increasingly used to describe, evaluate and interpret places, yet it remains unclear whether they do so from a culturally neutral standpoint. Here we test urban perception in frontier LLMs using a balanced global street-view sample and prompts that either remain neutral or invoke different regional cultural standpoints. Across open-ended descriptions and structured place judgments, the neutral condition proved not to be neutral in practice. Prompts associated with Europe and Northern America remained systematically closer to the baseline than many non-Western prompts, indicating that model perception is organized around a culturally uneven reference frame rather than a universal one. Cultural prompting also shifted affective evaluation, producing sentiment-based ingroup preference for some prompted identities. Comparisons with regional human text-image benchmarks showed that culturally proximate prompting could improve alignment with human descriptions, but it did not recover human levels of semantic diversity and often preserved an affectively elevated style. The same asymmetry reappeared in structured judgments of safety, beauty, wealth, liveliness, boredom and depression, where model outputs were interpretable but only partly reproduced human group differences. These findings suggest that LLMs do not simply perceive cities from nowhere: they do so through a culturally uneven baseline that shapes what appears ordinary, familiar and positively valued.