Integrating remote sensing and social science

Taubenböck, Hannes; Wurm, Michael; Setiadi, Neysa J.; Gebert, Niklas; Roth, Achim; Strunz, Günter; Birkmann, Joern; Dech, Stefan

doi:10.1109/urs.2009.5137506

Cited by 26 publications

(22 citation statements)

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“…In this review, we use the term "slum" to refer to urban areas with poor living conditions as this term expresses explicitly physical characteristics such as high densities or irregular patterns, indicators that can be derived by means of RS methods. Here, an ontological framework (e.g., developed by [5,7]) "provides a comprehensive description of spatial characteristics and their relationships to represent and characterize slums in an image" ( [31], p. 155). Such an ontology framework-split in three phases: specification, conceptualization and implementation [5]-provides a clear conceptual foundation for developing robust image-based indicators, facilitating global knowledge acquisition and comparisons for the development of a global slum inventory.…”

Section: Terminological Differencesmentioning

confidence: 99%

“…Often, researchers aim at the provision of basic information on "where" the slums are located within the urban fabric and what their areal extent is. Such information allows compensating for the non-availability of socioeconomic information (e.g., income levels) in many cities of the Global South [31]. Besides its importance for urban development [68], the where question is also relevant within a humanitarian context, for which several studies [67,76,109] developed methods to map refugee camps (e.g., under Copernicus) [125].…”

Section: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

“…Therefore, recently, some researchers have been exploring different slum typologies based on building sizes, density, pattern or location (Table 5). However, slums are often not the worst off areas in terms of socioeconomic conditions [31,114,161]. Thus, such typologies include also fuzzy classes (i.e., semi-formal), reflecting the dilemma that some areas are formal but are physically and/or socioeconomically similar to slums, e.g., high-density resettlement colonies in Delhi [54].…”

Section: The Diversity Of Slumsmentioning

confidence: 99%

“…Only English-language papers are selected, and very similar publications by the same authors (e.g., journal and conference publication) were counted only once. In total, 87 key publications ( [3][4][5][6][7]10,23,25,[31][32][33][38][39][40][41][43][44][45][46] The analytical framework ( Figure 2) for analyzing the retrieved publications, inspired by the outcome of the expert meeting on slum mapping [36], forms the skeleton for this review. Figure 2 shows that slum formation and development in global cities are impacted by several contextual factors, Remote Sens.…”

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confidence: 99%

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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: Terminological Differencesmentioning

confidence: 99%

Section: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

Section: The Diversity Of Slumsmentioning

confidence: 99%

mentioning

confidence: 99%

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

2016

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show abstract

“…Therefore, recently, some researchers have been exploring different slum typologies based on building sizes, density, pattern or location (Table 2.5). However, slums are often not the worst off areas in terms of socioeconomic conditions (Baud et al, 2008;Taubenböck et al, 2008;Taubenböck, Wurm, et al, 2009). Thus, such typologies include also fuzzy classes (i.e., semi-formal), reflecting the dilemma that some areas are formal but are physically and/or socioeconomically similar to slums, e.g., high-density resettlement colonies in Delhi .…”

Section: The Diversity Of Slumsmentioning

confidence: 99%