Sefala, Raesetje Bonjo2022-02-142022-02-142020https://hdl.handle.net/10539/32742A dissertation submitted in ful lfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, 2020Removing many of the legacies of Apartheid, a former policy of political and economic discrimination against non-European groups in South Africa, is a primary concern for the country. Aerial images of residential areas show the clear legacy of spatial apartheid, with completely segregated neighbourhoods of townships next to gated wealthy neighbourhoods, a phenomena which has largely remained una ected by the ending of apartheid. This research uses computer vision to analyse 698; 544 satellite images of 9 provinces in South Africa, taking the rst steps toward examining the evolution of spatial apartheid. To achieve this goal, we rst introduce a new dataset consisting of polygons demarcating land use, geographically labelled coordinates of all buildings in South Africa, and high resolution satellite imagery covering the entire country from 2006-2017. Using this dataset, we trained a UNet based semantic segmentation model to detect and classify clusters of buildings for 12 types of classes: Township, Suburb, Industrial area, Commercial land, Informal area, Farm, Collective living Quarters, Village, Smallholdings and Background. We classify these neighbourhoods with an accuracy of 57:45% and a Cohen's Kappa value of 0:4326, giving us the potential to investigate areas a ected by the Group Areas Act which enforced spatial apartheid/segregation.enThesis