Researchers from Stanford University and the World Bank’s Development Economics Data Group have introduced a groundbreaking approach to measuring poverty, leveraging deep learning and satellite imagery to address the shortcomings of traditional survey-based methods. Accurate and timely poverty assessments are crucial for global development, yet national censuses and household surveys are expensive and infrequent. As a result, many low- and middle-income countries struggle to obtain granular, up-to-date economic data. This study presents an innovative solution by integrating artificial intelligence and remote sensing, enabling policymakers to track economic conditions with greater accuracy, speed, and scalability.
About the Author
