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Remote Sens. 2017, 9(4), 384; doi:10.3390/rs9040384

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

1
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
2
Faculty of Social and Behavioural Sciences, University of Amsterdam, P.O. Box 15629, 1001 NC Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 1 February 2017 / Revised: 3 April 2017 / Accepted: 13 April 2017 / Published: 19 April 2017
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Abstract

Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management. View Full-Text
Keywords: deprivation; slum; informal settlement; urban remote sensing; logistic regression; random forest classifier; Mumbai deprivation; slum; informal settlement; urban remote sensing; logistic regression; random forest classifier; Mumbai
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kuffer, M.; Pfeffer, K.; Sliuzas, R.; Baud, I.; Maarseveen, M.V. Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai. Remote Sens. 2017, 9, 384.

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