Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being

Can features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? This study investigates that question by extracting both object and texture features from satellite images of Sri Lanka. These features are used to estimate poverty rates and average expected log consumption based on small-area estimates derived from census data for 1,291 administrative units.

Extracted features include:

• number and density of buildings
• prevalence of building shadows (proxying building height)
• number of cars
• length of roads
• types of agriculture
• roof material
• multiple texture and spectral indicators

A linear regression model explains 49–61% of variation in average expected log consumption and 37–62% of variation in poverty rates. Estimates remain accurate across the consumption distribution and when extrapolating predictions into adjacent areas. Performance declines when fewer households are used to estimate ground-truth measures of poverty and welfare.