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Constructing a visual dataset to study the effects of spatial apartheid in South Africa

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dc.contributor.author Sefala, R
dc.contributor.author Gebru, T
dc.contributor.author Mfupe, Luzango P
dc.contributor.author Moorosi, N
dc.contributor.author Klein, R
dc.date.accessioned 2022-08-08T07:22:51Z
dc.date.available 2022-08-08T07:22:51Z
dc.date.issued 2021-12
dc.identifier.citation Sefala, R., Gebru, T., Mfupe, L.P., Moorosi, N. & Klein, R. 2021. Constructing a visual dataset to study the effects of spatial apartheid in South Africa. http://hdl.handle.net/10204/12461 . en_ZA
dc.identifier.uri http://hdl.handle.net/10204/12461
dc.description.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, 550 of which are labeled. 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 over two years, which includes pixel-wise labels for 4 classes of neighborhoods: wealthy areas, non wealthy areas, nonresidential neighborhoods and background (land without buildings). 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. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://datasets-benchmarks-proceedings.neurips.cc/paper/2021 en_US
dc.relation.uri https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/07e1cd7dca89a1678042477183b7ac3f-Paper-round2.pdf en_US
dc.relation.uri https://openreview.net/forum?id=WV0waZz9dTF en_US
dc.relation.uri https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/07e1cd7dca89a1678042477183b7ac3f-Abstract-round2.html en_US
dc.source Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual, 6-14 December 2021 en_US
dc.subject Datasets en_US
dc.subject Satellite imagery en_US
dc.subject Segmentation en_US
dc.title Constructing a visual dataset to study the effects of spatial apartheid in South Africa en_US
dc.type Conference Presentation en_US
dc.description.pages 14 en_US
dc.description.note Paper presented at the 35th Conference on Neural Information Processing Systems (NeurIPS), Virtual, 6-14 December 2021 en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea Spectrum Access Mgmt Innov en_US
dc.identifier.apacitation Sefala, R., Gebru, T., Mfupe, L. P., Moorosi, N., & Klein, R. (2021). Constructing a visual dataset to study the effects of spatial apartheid in South Africa. http://hdl.handle.net/10204/12461 en_ZA
dc.identifier.chicagocitation Sefala, R, T Gebru, Luzango P Mfupe, N Moorosi, and R Klein. "Constructing a visual dataset to study the effects of spatial apartheid in South Africa." <i>Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual, 6-14 December 2021</i> (2021): http://hdl.handle.net/10204/12461 en_ZA
dc.identifier.vancouvercitation Sefala R, Gebru T, Mfupe LP, Moorosi N, Klein R, Constructing a visual dataset to study the effects of spatial apartheid in South Africa; 2021. http://hdl.handle.net/10204/12461 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Sefala, R AU - Gebru, T AU - Mfupe, Luzango P AU - Moorosi, N AU - Klein, R AB - 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, 550 of which are labeled. 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 over two years, which includes pixel-wise labels for 4 classes of neighborhoods: wealthy areas, non wealthy areas, nonresidential neighborhoods and background (land without buildings). 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. DA - 2021-12 DB - ResearchSpace DP - CSIR J1 - Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), Virtual, 6-14 December 2021 KW - Datasets KW - Satellite imagery KW - Segmentation LK - https://researchspace.csir.co.za PY - 2021 T1 - Constructing a visual dataset to study the effects of spatial apartheid in South Africa TI - Constructing a visual dataset to study the effects of spatial apartheid in South Africa UR - http://hdl.handle.net/10204/12461 ER - en_ZA
dc.identifier.worklist 25465 en_US


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