Preprints
https://doi.org/10.5194/essd-2025-260
https://doi.org/10.5194/essd-2025-260
28 May 2025
 | 28 May 2025
Status: this preprint is currently under review for the journal ESSD.

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

Dan Li, Laixiang Sun, Yang Yu, and Peipei Tian

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)).

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Dan Li, Laixiang Sun, Yang Yu, and Peipei Tian

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2025-260', Anonymous Referee #1, 13 Jun 2025 reply
    • AC1: 'Reply on RC1', Laixiang Sun, 16 Aug 2025 reply
  • RC2: 'Comment on essd-2025-260', Anonymous Referee #2, 25 Jun 2025 reply
    • AC2: 'Reply on RC2', Laixiang Sun, 16 Aug 2025 reply
Dan Li, Laixiang Sun, Yang Yu, and Peipei Tian

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Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data Dan Li https://zenodo.org/records/13779003

Dan Li, Laixiang Sun, Yang Yu, and Peipei Tian

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Short summary
We present a standardized, bottom-up framework to estimate slum populations at neighborhood level across 129 Global South countries. Our approach addresses underestimations in prior studies that rely heavily on slum geometry. Our dataset offers the first comprehensive inventory in data-sparse settings. This research offers valuable insights to support sustainable urban development goals, inform humanitarian aid distribution, and enhance the well-being in underserved communities.
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