地理空间基础模型的近期进展凸显了为现实世界地点（尤其是人类活动密集的兴趣点，POI）学习通用表征的重要性。然而，现有方法主要依赖静态文本元数据推断地点身份，或学习与轨迹上下文绑定的表征——这类表征反映的是移动规律，而非地点的实际用途（即POI的功能）。我们认为，POI功能是当前通用POI表征中缺失但至关重要的信号。为此，我们提出“嵌入移动性的兴趣点”（Mobility-Embedded POIs, ME-POIs）框架：该框架将语言模型生成的POI嵌入与大规模人类移动数据相结合，学习以POI为中心、上下文无关、且扎根于真实使用场景的表征。ME-POIs将单次访问编码为具有时间上下文的嵌入，并通过对比学习将其对齐至可学习的POI表征，从而捕捉跨用户与跨时间的使用模式。为缓解长尾稀疏性问题，我们设计了一种新机制，可在多个空间尺度上，将邻近高频访问POI的时间访问模式传播至目标POI。我们在五个新提出的地图增强任务上评估ME-POIs，检验其同时刻画POI身份与功能的能力。在所有任务中，将文本嵌入与ME-POIs融合均持续优于纯文本基线与纯移动性基线。值得注意的是，仅基于移动性数据训练的ME-POIs在部分任务上即可超越纯文本模型，表明POI功能是构建准确且泛化能力强的POI表征的关键组成部分。

Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates. Existing approaches, however, focus primarily on place identity derived from static textual metadata, or learn representations tied to trajectory context, which capture movement regularities rather than how places are actually used (i.e., POI's function). We argue that POI function is a missing but essential signal for general POI representations. We introduce Mobility-Embedded POIs (ME-POIs), a framework that augments POI embeddings derived, from language models with large-scale human mobility data to learn POI-centric, context-independent representations grounded in real-world usage. ME-POIs encodes individual visits as temporally contextualized embeddings and aligns them with learnable POI representations via contrastive learning to capture usage patterns across users and time. To address long-tail sparsity, we propose a novel mechanism that propagates temporal visit patterns from nearby, frequently visited POIs across multiple spatial scales. We evaluate ME-POIs on five newly proposed map enrichment tasks, testing its ability to capture both the identity and function of POIs. Across all tasks, augmenting text-based embeddings with ME-POIs consistently outperforms both text-only and mobility-only baselines. Notably, ME-POIs trained on mobility data alone can surpass text-only models on certain tasks, highlighting that POI function is a critical component of accurate and generalizable POI representations.