dc.contributor.author |
Sefala, R
|
|
dc.contributor.author |
Gebru, T
|
|
dc.contributor.author |
Mfupe, Luzango P
|
|
dc.contributor.author |
Moorosi, N
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|
dc.contributor.author |
Klein, R
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|
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 |