This project aims to enhance our understanding of the pace of agricultural and rural transformation in contemporary sub-Saharan Africa, its poverty and distributional impacts and drivers. The project addresses a longstanding and to date unresolved theoretical question in agrarian studies, namely whether agricultural transformation is poverty driven as proposed by neo-Marxist perspectives or if it enables inclusive growth as propounded by advocates of the pro-poor, agricultural growth model. Building on the methodological advances in machine learning and artificial intelligence, we combine training data from our existing survey-based database (Afrint), covering around 3 000 households from six African countries, distributed over sixteen different regions, 54 villages and spanning fifteen years of development. Drawing on this unique combination of data sources and methods, we will be able to provide new insights into the distributional effects of agricultural transformation, using a variety of established welfare indicators from the field of rural development studies, but breaking new ground in development research by collecting them through remote sensing techniques. This innovative mixed methods 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.