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Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
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VHR-GSIS: A Deep Learning Based Very High-Resolution Dataset for Informal Settlements in 37 Global South Cities (2010 & 2025) Using Harmonized Google Earth Imagery

Authors: Jiang, MingYu; Mohammad, Pir; Weng, Qihao;

VHR-GSIS: A Deep Learning Based Very High-Resolution Dataset for Informal Settlements in 37 Global South Cities (2010 & 2025) Using Harmonized Google Earth Imagery

Abstract

This study develops a harmonized, very‑high‑resolution (VHR) informal settlement mapping product covering 37 major cities across Africa, Asia, and Latin America. Building on a detailed assessment of regional morphological typologies, we operationalize two core UN‑Habitat criteria—housing durability and building density—as universal principles for informal settlement mapping. Nine representative cities are selected to construct independent segmentation models, each trained on a newly compiled dataset totally 12,053 manually curated image–label pairs derived from 0.59‑m Google Earth imagery, forming one of the largest VHR informal settlement datasets to date.The products are provided as vector (shapefile) products derived from post‑processed raster classifications. Each shapefile contains a categorical “status” field with three values: 0, 1, and 2. A value of 0 denotes areas where informal settlements extent remained unchanged between 2010 and 2025; a value of 1 represents areas that existed only in 2010 and had disappeared by 2025; and a value of 2 represents areas that newly appeared in 2025 relative to 2010. This structure enables users to directly quantify stable, lost, and newly emerged informal settlements patches across the two epochs.

Keywords

Slum, Informal settlements

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average