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

arXiv: https://arxiv.org/abs/2609.28154 · HTML: https://mlsanity.com/paper/2609.28154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)
- [Continental-Scale Building Detection from High Resolution Satellite Imagery](https://mlsanity.com/paper/2107.12283/) (2021)
- [GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models](https://mlsanity.com/paper/2506.04106/) (2025)
- [High-Resolution Building and Road Detection from Sentinel-2](https://mlsanity.com/paper/2310.11622/) (2023)
- [The SpaceNet Multi-Temporal Urban Development Challenge](https://mlsanity.com/paper/2102.11958/) (2021)
- [Outlining where humans live -- The World Settlement Footprint 2015](https://mlsanity.com/paper/1910.12707/) (2019)
- [TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery](https://mlsanity.com/paper/2511.12104/) (2025)
- [Evaluating the Quality of Open Building Datasets for Mapping Urban Inequality: A Comparative Analysis Across 5 Cities](https://mlsanity.com/paper/2508.12872/) (2025)
- [Global Building Area Estimation Products: How Accurate Are They?](https://mlsanity.com/paper/2607.19766/) (2026)

## Abstract (arXiv)

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.