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The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Department of Social Statistics and Department of Geography, University of Southampton, Highfield Campus, Building 58, Southampton SO17 1BJ, UK
WorldPop Research Group, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
Department of Computational and Data Sciences, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
Department of Geosciences, Environment and Society, Université libre de Bruxelles (ULB), 1050 Bruxelles, Belgium
Department of Geography, George Washington University, Washington, DC 20052, USA
Global Urban Observatory, UN-Habitat, 30030-00100 Nairobi, Kenya
Slum Dwellers International, 20509-00100 Nairobi, Kenya
Institute for Global Sustainable Development, University of Warwick, Coventry CV4 7AL UK
The Alan Turing Institute, British Library, London NW1 2DB, UK
African Population & Health Research Center, 10787-00100 Nairobi, Kenya
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 982;
Received: 18 February 2020 / Revised: 10 March 2020 / Accepted: 11 March 2020 / Published: 18 March 2020
Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups. View Full-Text
Keywords: deprived areas; slums; informal settlement; machine learning; urban remote sensing deprived areas; slums; informal settlement; machine learning; urban remote sensing
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Kuffer, M.; Thomson, D.R.; Boo, G.; Mahabir, R.; Grippa, T.; Vanhuysse, S.; Engstrom, R.; Ndugwa, R.; Makau, J.; Darin, E.; de Albuquerque, J.P.; Kabaria, C. The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries. Remote Sens. 2020, 12, 982.

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