Monitoring regional development in data-poor countries: Integrating satellite images, socioeconomic surveys, and machine learning

Abstract

Monitoring regional development in countries with limited traditional data sources poses a significant obstacle for tracking the achivement of the sustainable development goals. Standard approaches depend heavily on socioeconomic surveys, which can be expensive and logistically difficult in poor subnational regions. This presentation shows how integrating earth observation data, socioeconomic surveys, and machine learning techniques can mitigate these challenges. .

Carlos Mendez

Associate Professor of Development Economics

My research interests focus on the integration of development economics, spatial data science, and econometrics to understand and inform the process of sustainable development across subnational regions and countries.