Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data

Abstract. Slums are a visible manifestation of poverty in Global South countries. Reliable estimation of slum population is crucial for urban planning, humanitarian aid provision, and improving well-being. However, large-scale and fine-grained mapping is still lacking due to inconsistent methodologies and definitions across countries. Existing datasets often rely on government statistics, lacking spatial continuity or underestimating slum population due to factors such as city image and privacy concerns. Here, we develop a standardized bottom-up approach to estimate slum population at the neighborhood level (~6.72 km resolution at the equator) for 129 Global South countries in 2018. Leveraging the Sustainable Development Goals 11.1 framework and machine learning, our estimation integrates household-based surveys, satellite imagery, and grided population data. Our models explain 82 % to 96 % of the variation in ground-truth surveys, with a root mean squared error of 4.85 % to 10.47 %, outperforming previous benchmarks. Cross-validation with independent data confirms the reliability of our estimates. To our knowledge, this is the first comprehensive geospatial inventory of slum populations across Global South countries, offering valuable insights for advancing urban sustainability and supporting further research on vulnerable populations. (https://doi.org/10.5281/zenodo.13779003 (Li et al., 2025)).