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Remote Sens. 2017, 9(9), 895;

Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery

Research in Spatial Economics (RiSE-Group), Department of Economics, Universidad EAFIT, Carrera 49 No. 7 Sur-50, 050022 Medellin, Colombia
Department of Engineering (DITEN), University of Genova, 16145 Genova, Italy
Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Author to whom correspondence should be addressed.
Academic Editor: Qi Wang
Received: 26 July 2017 / Revised: 24 August 2017 / Accepted: 25 August 2017 / Published: 30 August 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Slum identification in urban settlements is a crucial step in the process of formulation of pro-poor policies. However, the use of conventional methods for slum detection such as field surveys can be time-consuming and costly. This paper explores the possibility of implementing a low-cost standardized method for slum detection. We use spectral, texture and structural features extracted from very high spatial resolution imagery as input data and evaluate the capability of three machine learning algorithms (Logistic Regression, Support Vector Machine and Random Forest) to classify urban areas as slum or no-slum. Using data from Buenos Aires (Argentina), Medellin (Colombia) and Recife (Brazil), we found that Support Vector Machine with radial basis kernel delivers the best performance (with F2-scores over 0.81). We also found that singularities within cities preclude the use of a unified classification model. View Full-Text
Keywords: remote sensing; slum detection; machine learning remote sensing; slum detection; machine learning

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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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Duque, J.C.; Patino, J.E.; Betancourt, A. Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery. Remote Sens. 2017, 9, 895.

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