A comparative assessment of global building and settlement datasets across geographic and settlement contexts

Global building and settlement products increasingly support population mapping, exposure assessment, and urban monitoring, yet comparative evidence remains fragmented across representations, geographic contexts, and evaluation methods. We benchmark seven global or near-global products against harmonized reference building footprints across 135 study areas. Our representation-aware framework integrates complementary measures of detection, geometric agreement, and aggregate quantity accuracy with stratified analyses of settlement characteristics, evaluation resolution, reference provenance, and temporal alignment. Overture Maps achieved the highest median city-level vector F1 (0.786), while 3D-GloBFP showed the closest aggregate building-area agreement despite weaker detection performance. Raster rankings were scale-dependent: Google Open Buildings Temporal (OBT) performed best at 10,m (F1 = 0.680), whereas WSF Tracker led at 100,m (F1 = 0.904) but substantially overestimated reference building area (relative bias = 1.845). Raster accuracy increased with building density (ρ=0.58--0.75), while smaller candidate footprints were disproportionately associated with vector false positives. Temporal alignment improved WSF Tracker mean city-level F1 by 0.060. These findings demonstrate that product reliability depends on representation, spatial scale, and settlement context, providing a reproducible framework for assessing the suitability of EO-derived urban information for different applications.