地理空间基础模型（GFMs）正成为学习语义丰富且地理一致的视觉与物理表征的一种强大范式。然而，其对地球观测（EO）数据的依赖导致人类活动相关信息在很大程度上未被充分表征。人类移动数据揭示了区域间功能与关系结构，而此类信息在EO数据中缺失；但该类数据通常仅覆盖单一城市，因而难以用于可迁移的城市表征学习。本文提出MoRAX——一种轻量级框架，用于将源自人类移动性的功能结构融入地理空间嵌入。MoRAX在保持GFM覆盖范围与一致性的同时，提供城市区域间功能连通性信息，从而支持零样本部署于未见城市，无论目标城市是否具备可用的移动数据。在横跨两个国家的四座目标城市上，观察移动数据的MoRAX教师模型在八项社会经济与环境预测任务中持续优于GFMs及强城市表征基线模型。与此同时，从不以移动数据为输入的学生模型在多数任务中性能接近教师模型。跨国家的迁移结果进一步表明，以移动流为条件的调制机制为将地理空间基础模型锚定于城市的人本维度提供了一种通用途径。

Geospatial Foundation Models (GFMs) are emerging as a powerful paradigm for learning semantically rich and geographically consistent visual and physical representations. However, their reliance on Earth-observation (EO) data leaves information about human activity largely underrepresented. Human mobility data reveals the functional and relational structure between regions that is missing from EO data, but is often limited only to the city where it is observed, making it challenging to use for transferable urban representation learning. We introduce MoRAX, a lightweight framework for augmenting geospatial embeddings with functional structure derived from human mobility. MoRAX preserves the coverage and consistency of a GFM while providing information about the functional connectivity among urban regions, permitting zero-shot deployment in unseen cities with or without available mobility data. Across four target cities spanning two countries, the MoRAX teacher model, which observes mobility, consistently outperforms GFMs and strong urban representation baselines in eight socioeconomic and environmental prediction tasks. Meanwhile, the student model, which never takes mobility data as input, approaches the teacher in performance on most tasks. Transfer results across countries further demonstrate that modulation conditioned on mobility flows provides a general mechanism for grounding geospatial foundations in the human dimension of cities.