A Bird’s-Eye View on Agricultural Transformation in sub-Saharan Africa: An analysis of living standards indicators combining panel data, satellite imagery and deep learning

The last major area in the world where poverty and all of its related issues seem intractable is sub- Saharan Africa. It has long been a problem that fundamental and detailed data regarding development indicators and their distribution over space and time and among different segments of the population are unavailable. This is especially a concern in rural areas and among the farming population. In this project we address longstanding and unresolved questions in development research regarding the distributional effects of rural transformations, poverty and production levels. We do this through an innovative framework featuring a new application of artificial intelligence techniques. More precisely, we apply machine learning to satellite imagery along with more conventional panel survey data. This is the first study of its kind and combines expertise from distant disciplines such as physics, development research and remote sensing in a cross-disciplinary effort.
This innovative approach will also provide a real-life contribution to addressing a practical problem of collecting statistics on the ground in developing countries that lack infrastructure or administrative resources. The project hence will be able to fill the data-collection needs inherent in monitoring a number of the Sustainable Development Goals.