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Remote Sensing
Volume 12
Issue 23
10.3390/rs12233847
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Article

Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings

by
Christopher T. Lloyd
1,*,
Hugh J. W. Sturrock
2,
Douglas R. Leasure
1,
Warren C. Jochem
1,
Attila N. Lázár
1 and
Andrew J. Tatem
1
1
WorldPop Programme, Department of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK
2
Locational, Lytchett House, 13 Freeland Park, Wareham Road, Poole BH16 6FA, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(23), 3847; https://doi.org/10.3390/rs12233847
Received: 28 August 2020 / Revised: 13 November 2020 / Accepted: 19 November 2020 / Published: 24 November 2020
(This article belongs to the Special Issue Remote Sensing Application to Population Mapping)
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Abstract

Utilising satellite images for planning and development is becoming a common practice as computational power and machine learning capabilities expand. In this paper, we explore the use of satellite image derived building footprint data to classify the residential status of urban buildings in low and middle income countries. A recently developed ensemble machine learning building classification model is applied for the first time to the Democratic Republic of the Congo, and to Nigeria. The model is informed by building footprint and label data of greater completeness and attribute consistency than have previously been available for these countries. A GIS workflow is described that semiautomates the preparation of data for input to the model. The workflow is designed to be particularly useful to those who apply the model to additional countries and use input data from diverse sources. Results show that the ensemble model correctly classifies between 85% and 93% of structures as residential and nonresidential across both countries. The classification outputs are likely to be valuable in the modelling of human population distributions, as well as in a range of related applications such as urban planning, resource allocation, and service delivery. View Full-Text
Keywords: machine learning; building classification; superlearner; residential; building footprint machine learning; building classification; superlearner; residential; building footprint
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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.

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MDPI and ACS Style

Lloyd, C.T.; Sturrock, H.J.W.; Leasure, D.R.; Jochem, W.C.; Lázár, A.N.; Tatem, A.J. Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings. Remote Sens. 2020, 12, 3847. https://doi.org/10.3390/rs12233847

AMA Style

Lloyd CT, Sturrock HJW, Leasure DR, Jochem WC, Lázár AN, Tatem AJ. Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings. Remote Sensing. 2020; 12(23):3847. https://doi.org/10.3390/rs12233847

Chicago/Turabian Style

Lloyd, Christopher T., Hugh J. W. Sturrock, Douglas R. Leasure, Warren C. Jochem, Attila N. Lázár, and Andrew J. Tatem. 2020. "Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings" Remote Sensing 12, no. 23: 3847. https://doi.org/10.3390/rs12233847

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