Geospatial foundation-model embeddings improve population estimation unevenly across space and scale

TL;DR

This study benchmarks a geospatial foundation model for population estimation, showing it improves accuracy in data-sparse areas but faces limitations with scale mismatches across different regions.

Contribution

It demonstrates the effectiveness and limitations of foundation-model embeddings compared to traditional covariates in subnational population estimation.

Findings

PDFM increased predictive fit by median 20.1% across countries.

PDFM reduced Kullback-Leibler divergence by 23.2%.

Performance was most advantageous in areas with weak traditional covariates.

Abstract

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subnational population estimates are essential for applications, yet remain difficult where censuses are sparse, outdated or spatially coarse.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.