Poverty mapping in Mongolia with AI-based Ger detection reveals urban slums persist after the COVID-19 pandemic

TL;DR

This paper presents an AI-based satellite imagery analysis method to detect ger settlements in Mongolia, revealing persistent urban slums post-COVID-19 and demonstrating strong correlation with poverty data.

Contribution

The study introduces a novel computer vision algorithm for detecting ger dwellings in satellite images, enabling accurate mapping of urban slums in Mongolia.

Findings

Ger settlements are displaced to city peripheries.

Slum ratio correlates strongly with poverty data (r=0.84).

One-fifth of the population may still live in slums post-pandemic.

Abstract

Mongolia is among the countries undergoing rapid urbanization, and its temporary nomadic dwellings-known as Ger-have expanded into urban areas. Ger settlements in cities are increasingly recognized as slums by their socio-economic deprivation. The distinctive circular, tent-like shape of gers enables their detection through very-high-resolution satellite imagery. We develop a computer vision algorithm to detect gers in Ulaanbaatar, the capital of Mongolia, utilizing satellite images collected from 2015 to 2023. Results reveal that ger settlements have been displaced towards the capital's peripheral areas. The predicted slum ratio based on our results exhibits a significant correlation (r = 0.84) with the World Bank's district-level poverty data. Our nationwide extrapolation suggests that slums may continue to take up one-fifth of the population after the COVID-19 pandemic, contrary to other official predictions that anticipated a decline. We discuss the potential of machine learning on satellite imagery in providing insights into urbanization patterns and monitoring the Sustainable Development Goals.