Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa

Raesetje Sefala, Timnit Gebru, Luzango Mfupe, Nyalleng Moorosi, Richard Klein

Keywords: satellite imagery, datasets, segmentation

TL;DR: Dataset creation methodology to construct a visual dataset to study Spatial Apartheid in South Africa.

Abstract: Aerial images of neighborhoods in South Africa show the clear legacy of Apartheid, a former policy of political and economic discrimination against non-European groups, with completely segregated neighborhoods of townships next to gated wealthy areas. This paper introduces the first publicly available dataset to study the evolution of spatial apartheid, using 6,768 high resolution satellite images of 9 provinces in South Africa. Our dataset was created using polygons demarcating land use, geographically labelled coordinates of buildings in South Africa, and high resolution satellite imagery covering the country from 2006-2017. We describe our iterative process to create this dataset, which includes pixel wise labels for 4 classes of neighborhoods: wealthy areas, non wealthy areas, non residential neighborhoods and vacant land. While datasets 7 times smaller than ours have cost over 1M to annotate, our dataset was created with highly constrained resources. We finally show examples of applications examining the evolution of neighborhoods in South Africa using our dataset.

URL: https://github.com/sefalab/Spatial_Project