Large-scale slum mapping in sub-Saharan Africa's major cities: Remote sensing and deep learning reveal strong slum growth in the urban periphery between 2016 and 2022
Nicolas Büttner, Steven Stalder, Michele Volpi, Esra Süel, Kenneth Harttgen
Abstract
Around half of sub-Saharan Africa's urban population lives in slums, yet data on the spatiotemporal development of slums remains scarce, impeding policies to alleviate urban poverty and inequality. We propose a solution to this problem by applying deep learning to open-access satellite imagery to map slums in 529 major cities across sub-Saharan Africa and track their spatiotemporal development. Our model produced 10m resolution ‘slum probability maps’ allowing timely and cost-effective tracking of slum growth. On this basis, we estimated that in 2022 the share of the urban population living in slums exceeded 50% in 274 cities, and in 84% of cities this share increased between 2016 and 2022, most severely in Middle and West Africa. Slum growth occurred primarily in the urban periphery, which tends to be missed in survey-based slum monitoring. • Applying deep learning to open-access satellite images for large-scale slum mapping. • Over 50 % slum dwellers in half of Africa's major cities in 2022. • Slum dweller share increased in most of Africa's major cities from 2016 to 2022. • Africa's slums are growing primarily in the urban periphery. • Survey-based slum tracking is likely to miss significant shares of slum dwellers.