Image Sources for Informal Settlement Management

Mason, Scott; Fraser, Clive S.

doi:10.1111/0031-868x.00128

Cited by 33 publications

(20 citation statements)

References 3 publications

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“…Figure 6, [41,176,177] ) need to be more systematically explored [177]. Already in 1998, Mason and Fraser [178] specified three main characteristics of an effective system to map/monitor slums, specifically low-cost (data acquisition and processing), semi-automated processes (fast and reliable results) and simple usage by low-skilled operators (standard software). The analysis of the employed methods in the reviewed slum publications shows that most studies used commercial and rather expensive imagery.…”

Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

2016

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Abstract:The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000-2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area-and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.Keywords: slums; informal areas; urban remote sensing; Global South; VHR imagery Global Urbanization and Slum Dynamics: The ContextCurrently, about one-quarter of the world's urban population lives in slums, which are defined by UN-Habitat as informal settlements [1] or areas deprived of access to safe water, acceptable sanitation, and durable housing; in addition to being areas that are overcrowded and lack land tenure security [2]. Over the last 15 years, there has been renewed interest in slum improvement and eradication by local and international organizations dealing with development issues. During this period, slums became a more prominent subject of remote sensing (RS) image analysis. Supported by increased availability of very-high-resolution (VHR) data and methodological advances, many RS studies [3][4][5][6][7][8] aimed to produce information on the geography and dynamics of slums. ...

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Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

2016

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“…Already in 1998, Mason and Fraser (1998) specified three main characteristics of an effective system to map/monitor slums, specifically low-cost (data acquisition and processing), semi-automated processes (fast and reliable results) and simple usage by low-skilled operators (standard software). The analysis of the employed methods in the reviewed slum publications shows that most studies used commercial and rather expensive imagery.…”

Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

Spatial patterns of deprivation in cities of the global south in very high resolution imagery

Kuffer

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“…Recently very high resolution remote sensing based methods for mapping slums are getting popular among the scientific community (Mason& Fraser, 1998;Sliuzas, Kerle and Kuffer, 2008;Hofmann .P., 2001;Hofmann. P., 2004 et al ) but there are only very few studies based on Indian situation (Ujjwal Sur, 2004).…”

Section: Related Work On Detecting Slumsmentioning

confidence: 99%

Detecting Slums From Quick Bird Data in Pune Using an Object Oriented Approach

Shekhar

2012

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

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ABSTRACT:We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To car ry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the cl assification rule base led to the best classification with more than 80% accuracy.

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Image Sources for Informal Settlement Management

Cited by 33 publications

References 3 publications

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

Spatial patterns of deprivation in cities of the global south in very high resolution imagery

Detecting Slums From Quick Bird Data in Pune Using an Object Oriented Approach

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