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

ArXi:2605.01650v1 Announce Type: new 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subnational population estimates are essential for applications, yet remain difficult where censuses are sparse, outdated or spatially coarse.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 multifaceted and heterogeneous data sources.