用地理基础模型提升人口估算，尤其在数据稀疏地区效果显著。
Geospatial foundation-model embeddings improve population estimation unevenly across space and scale
- 用地理基础模型提取空间表征，替代传统人工构建的地理变量。
- 在巴西、尼日利亚、美国平均降低20.1%未解释方差，减少23.2%KL散度。
- 在欠发达区域表现更好，但跨尺度迁移能力弱于传统方法。
可靠的次国家级人口估计对诸多应用至关重要，但在普查数据稀疏、过时或空间分辨率低的地区仍具挑战。现有方法依赖手工构建的地理变量（如聚落范围、夜间灯光、环境条件），需跨尺度与区域统一处理。地理基础模型通过从更复杂多样的数据中学习可复用的地点表征，提供新路径。本文以巴西、尼日利亚和美国为例，对比了人口动态基础模型（PDFM）嵌入与统一地理变量在次国家级人口估算中的表现。在结构化地理验证下，PDFM使预测拟合度中位提升20.1%（四分位距：10.0–33.2%），未解释方差减少；KL散度下降23.2%（9.2–26.2%）。然而收益分布不均：当传统变量难以刻画聚落背景时（如较大且欠发达区域），优势最明显。此外，PDFM性能受尺度耦合影响，其跨空间聚合的迁移灵活性低于地理变量。结果表明，地理基础模型能有效提升数据贫瘠地区的估算精度，但其优势在空间尺度不匹配时会系统性减弱，揭示当前地理人工智能的根本局限。
原文摘要 · Abstract (English)
Reliable subnational population estimates are essential for applications, yet remain difficult where censuses are sparse, outdated or spatially coarse. Existing population-mapping workflows rely on hand-built geospatial covariates, such as settlement extent, night-time lights, and environmental conditions, which must be assembled and harmonised across scales and geographies. Geospatial foundation models offer an alternative by learning reusable representations of place from more multifaceted and heterogeneous data sources. Here, we benchmark Population Dynamics Foundation Model (PDFM) embeddings against the harmonised geospatial covariates for subnational population estimation in Brazil, Nigeria and the United States. Under geographically structured validation, PDFM increased predictive fit by a median of 20.1% (IQR: 10.0-33.2%, across country-model comparisons) reduction in unexplained variance, and reduced Kullback-Leibler divergence by 23.2% (9.2-26.2%). However, these gains were uneven. PDFM was most advantageous where the geospatial covariates weakly characterised settlement context, such as larger and less-developed subnational areas. Moreover, PDFM performance was scale-coupled with embeddings providing less flexible transfer across spatial aggregations than geospatial covariates. These findings showed that geospatial foundation-model representations of place can improve population estimation in data poor settings, but their benefits break down predictably under spatial scale mismatch, revealing a fundamental limitation of current geospatial AI.
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