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

Juan Duque, Jorge Patiño and Alejandro Betancourt

No 975, Research Department working papers from CAF Development Bank Of Latinamerica

Abstract: Slum identification in urban settlements is a crucial step in the process of formulation of propoor policies. However, the use of conventional methods for slums detection such as field surveys may result 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 deliver the best performance (over 0.81). We also found that singularities within cities preclude the use of a unified classification model.

Keywords: Ciudades; Desarrollo urbano; Economía; Equidad e inclusión social; Georreferenciación; Investigación socioeconómica; Pobreza; Políticas públicas; Servicios públicos; Vivienda (search for similar items in EconPapers)
Date: 2016
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:dbl:dblwop:975

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