尽管在数据匮乏地区对社会经济指标的精确测量仍具挑战性，从而制约政策干预与资源分配，但高分辨率地理空间数据却广泛可得，并可能蕴含多种宜居性统计信息。我们探究了AlphaEarth、AnySat和TerraMind等基础模型嵌入所编码的物理特征，并提出一个系统性框架，以识别最具预测力的地理空间指标。通过分析不同类型地理空间数据对城市宜居性预测的影响，本方法使研究者能够针对其特定应用场景优先选用信息量最大的特征。此外，我们展示了如何利用基础模型嵌入提升此类结果的预测性能。本研究贡献了一种原理明确的方法，可在考虑复杂空间依赖关系的前提下，从卫星影像中提取可操作信息，适用于观测数据有限地区的城市宜居性预测。

While accurate measurement of socioeconomic indicators remains challenging in data-scarce regions, which limits policy interventions and resource allocation, high-resolution geospatial data is widely available and can contain information on various livability statistics. We investigate which physical features are encoded within foundation model embeddings, such as AlphaEarth, AnySat, and TerraMind, and provide a systematic framework for identifying the most predictive geospatial indicators. By analyzing how different types of geospatial data influence urban livability predictions, our approach enables researchers to prioritize the most informative features for their specific applications. Additionally, we demonstrate how to leverage foundation model embeddings to enhance prediction performance for these outcomes. This work contributes a principled methodology for extracting actionable information from satellite imagery while accounting for complex spatial dependencies, with applications in predicting urban livability in regions with limited observation data.