地理空间基础模型的最新进展突显了为现实世界位置（尤其是人类活动集中区域——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导场所身份，或学习与轨迹上下文相关的表征，这些表征捕捉的是移动规律而非场所的实际使用方式（即POI的功能）。我们认为，POI功能是构建通用POI表征所缺失但至关重要的信号。本文提出一种名为嵌入移动性的兴趣点（ME-POIs）的框架，通过将大规模人类移动数据与基于语言模型生成的POI嵌入相结合，学习以场所为中心、与上下文无关且基于真实使用场景的表征。ME-POIs将个体访问行为编码为具有时间上下文的嵌入，并通过对比学习将其与可学习的POI表征对齐，以捕捉跨用户和时间的使用模式。为应对长尾稀疏问题，我们提出一种新颖机制，能够在多个空间尺度上将邻近高频访问POI的时间访问模式进行传播。我们在五个新提出的地图增强任务上评估了ME-POIs，测试其捕捉POI身份与功能的能力。在所有任务中，将文本嵌入与ME-POIs结合均显著优于仅使用文本或仅使用移动数据的基线模型。值得注意的是，仅基于移动数据训练的ME-POIs在某些任务上甚至超越了纯文本模型，凸显了POI功能在构建准确且可泛化的POI表征中的关键作用。

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.