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

Bibliographic Data

ID23329447
AuthorsMonika Kuffer (0000-0002-1915-2069, University of Twente, corresponding author), Karin Pfeffer (0000-0002-6080-1323, University of Amsterdam), Richard Sliuzas (0000-0001-5243-4431, University of Twente)
Year2016
Volume8
Issue6
Pages455
Publication date2016-05-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueRemote Sensing (JOURNAL)
Journal identifiersISSN: 2072-4292 • E-ISSN: 2072-4292
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/rs8060455
OpenAlexW2404611670
LanguageEN
Citations received91
References cited134

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.

Context (archaeology) · Data mining · Data science · Geography · Machine learning · Pixel · Population · Remote sensing · Robustness (evolution) · Slum · Sustainable development · Artificial Intelligence · Computer Science · Land Use and Ecosystem Services · Remote-Sensing Image Classification · Urban and Rural Development Challenges

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