Slums from Space—15 Years of Slum Mapping Using Remote SensingReport as inadecuate

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Faculty of Geo-Information Science and Earth Observation ITC, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands


Faculty of Social and Behavioural Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands


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Academic Editors: Ioannis Gitas and Prasad S. Thenkabail

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. View Full-Text

Keywords: slums; informal areas; urban remote sensing; Global South; VHR imagery slums; informal areas; urban remote sensing; Global South; VHR imagery

Author: Monika Kuffer 1,* , Karin Pfeffer 2 and Richard Sliuzas 1



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