The upgrading of an informal settlement in South Africa: Two decades onwards

Marais, Lochner; Ntema, John

doi:10.1016/j.habitatint.2012.11.001

Cited by 62 publications

(31 citation statements)

References 31 publications

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“…In some situations, maps could be used to decide whether a slum should be eradicated and people moved to a more appropriate part of the city [59]. In addition, the resulting land use maps are useful in evaluating the sustainability in land use, land cover changes and transition when considered over a time series [60,61].…”

Section: Discussionmentioning

confidence: 99%

Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

et al. 2017

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Information about the location and extent of informal settlements is necessary to guide decision making and resource allocation for their upgrading. Very high resolution (VHR) satellite images can provide this useful information, however, different urban settlement types are hard to be automatically discriminated and extracted from VHR imagery, because of their abstract semantic class definition. State-of-the-art classification techniques rely on hand-engineering spatial-contextual features to improve the classification results of pixel-based methods. In this paper, we propose to use convolutional neural networks (CNNs) for learning discriminative spatial features, and perform automatic detection of informal settlements. The experimental analysis is carried out on a QuickBird image acquired over Dar es Salaam, Tanzania. The proposed technique is compared against support vector machines (SVMs) using texture features extracted from grey level co-occurrence matrix (GLCM) and local binary patterns (LBP), which result in accuracies of 86.65% and 90.48%, respectively. CNN leads to better classification, resulting in an overall accuracy of 91.71%. A sensitivity analysis shows that deeper networks result in higher accuracies when large training sets are used. The study concludes that training CNN in an end-to-end fashion can automatically learn spatial features from the data that are capable of discriminating complex urban land use classes.

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Section: Discussionmentioning

confidence: 99%

Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

et al. 2017

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“…In Marais & Ntema's (2013) reflection through the lens of Huchzermeyer (2004), discussions of this unfolds in a manner that suggests that until recently, policy had not been developed for the new post-apartheid government for informal settlement upgrading. 'Breaking New Ground': A comprehensive plan for the development of informal settlements was developed only in 2004, after the realisation that the housing subsidy scheme alone was not sufficient to deal with the large inflow of black people in informal settlements (Huchzermeyer, 2004).…”

Section: The Discourse Of Upgrading Informal Settlements Pre and Postmentioning

confidence: 99%

“…The land that was made available for black people was mostly funded by loans provided by the National Housing Commission. The large influx of people by the early 1990 in the form of land invasions on open land across South Africa (Seekings, 1991) put pressure on the apartheid government to embark on large-scale upgrading of informal settlements (Marais & Ntema, 2013). The Independent Development Trust (IDT) was then established around that time providing site-and service and settlement upgrading to approximately 100 000 households across South Africa providing water, sanitation, electricity and ownership, packaged in the capital subsidy of R7 500 per household (Marais & Ntema, 2013).…”

Section: The Discourse Of Upgrading Informal Settlements Pre and Postmentioning

confidence: 99%

“…The large influx of people by the early 1990 in the form of land invasions on open land across South Africa (Seekings, 1991) put pressure on the apartheid government to embark on large-scale upgrading of informal settlements (Marais & Ntema, 2013). The Independent Development Trust (IDT) was then established around that time providing site-and service and settlement upgrading to approximately 100 000 households across South Africa providing water, sanitation, electricity and ownership, packaged in the capital subsidy of R7 500 per household (Marais & Ntema, 2013). The upgrading process by the IDT, Huchzermeyer (2004) agues and critics, where driven by the market orientated development approach (neo-liberalism) with very minimal community participation as well as a technocratic approach of a one size-fits-all, putting people on the periphery in greenfield projects.…”

Section: The Discourse Of Upgrading Informal Settlements Pre and Postmentioning

confidence: 99%

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Mobile Gis: A Tool for Informal Settlement Occupancy Audit to Improve Integrated Human Settlement Implementation in Ekurhuleni, South Africa

Mokoena¹,

Musakwa²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission II, Theme Session 17 Smart CitiesKEY WORDS: Mobile GIS, occupancy audit, informal settlements, integration, Ekurhuleni ABSTRACT:Upgrading and relocating people in informal settlements requires consistent commitment, good strategies and systems so as to improve the lives of those who live in them. In South Africa, in order to allocate subsidised housing to beneficiaries of an informal settlement, beneficiary administration needs to be completed to determine the number of people who qualify for a subsidised house. Conventional methods of occupancy audits are often unreliable, cumbersome and non-spatial. Accordingly, this study proposes the use of mobile GIS to conduct these audits to provide up-to-date, accurate, comprehensive and real-time data so as to facilitate the development of integrated human settlements. An occupancy audit was subsequently completed for one of the communities in the Ekurhuleni municipality, Gauteng province, using web-based mobile GIS as a solution to providing smart information through evidence based decision making. Fieldworkers accessed the off-line capturing module on a mobile device recording GPS coordinates, socio-economic information and photographs. The results of this audit indicated that only 56.86% of the households residing within the community could potentially benefit from receiving a subsidised house. Integrated residential development, which includes fully and partially subsidised housing, serviced stands and some fully bonded housing opportunities, would then be key to adequately providing access to suitable housing options within a project in a post-colonial South Africa, creating new post-1994 neighbourhoods, in line with policy. The use of mobile GIS therefore needs to be extended to other informal settlement upgrading projects in South Africa.

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“…That is why a growing number of scholars (Fern andez-Güell, 2006;Marais & Ntema, 2013;Peris, AcebilloBaqu e, & Calabuig, 2011;Scott, Cotton, & Sohail Khan, 2013;Taleai, Sliuzas, & Flacke, 2014) have argued the need to take into consideration the complexity of these processes. The heterogeneous nature and intricate relationships of the functional elements and the stakeholders involved in urban processes add difficulty to the analysis.…”

Section: Introductionmentioning

confidence: 99%

Instruments for analysing urban conflicts in the Global South: The case of urban transformation processes in Dakar

Andrés

Güell

Smith

2015

Habitat International

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The upgrading of an informal settlement in South Africa: Two decades onwards

Cited by 62 publications

References 31 publications

Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

Mobile Gis: A Tool for Informal Settlement Occupancy Audit to Improve Integrated Human Settlement Implementation in Ekurhuleni, South Africa

Instruments for analysing urban conflicts in the Global South: The case of urban transformation processes in Dakar

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