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Found 3 papers, 1 papers with code
Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs
We infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach.
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use.
Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments.
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