Geospatial foundation-model embeddings improve population estimation unevenly across space and scale
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subnational population estimates are essential for applications, yet remain difficult where censuses are sparse, outdated or spatially coarse.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, ...
| Authors | Wenbin Zhang, Eimear Cleary, Francisco Rowe et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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