Welfare estimations from imagery. A test of domain experts ability to rate poverty from visual inspection of satellite imagery
Wahab Ibrahim, Ola Hall

TL;DR
This study evaluates how well domain experts can estimate poverty levels from satellite imagery and identifies visual features that correlate with wealth, aiming to improve machine learning models for poverty prediction.
Contribution
It demonstrates the feasibility of using visual indicators from satellite images for welfare estimation and highlights key features that predict wealth, advancing explainable AI in this domain.
Findings
Experts' ratings correlated with DHS wealth rankings.
Modern roofs and wider roads indicate higher wealth.
Poor road coverage and greenery correlate with lower wealth.
Abstract
The present study uses domain experts to estimate welfare levels and indicators from high-resolution satellite imagery. We use the wealth quintiles from the 2015 Tanzania DHS dataset as ground truth data. We analyse the performance of the visual estimation of relative wealth at the cluster level and compare these with wealth rankings from the DHS survey of 2015 for that country using correlations, ordinal regressions and multinomial logistic regressions. Of the 608 clusters, 115 received the same ratings from human experts and the independent DHS rankings. For 59 percent of the clusters, experts ratings were slightly lower. On the one hand, significant positive predictors of wealth are the presence of modern roofs and wider roads. For instance, the log odds of receiving a rating in a higher quintile on the wealth rankings is 0.917 points higher on average for clusters with buildings…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Land Use and Ecosystem Services · Impact of Light on Environment and Health
