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

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  • Duque, Juan Carlos
  • Patino, Jorge Eduardo
  • Betancourt, Alejandro

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.

Suggested Citation

  • Duque, Juan Carlos & Patino, Jorge Eduardo & Betancourt, Alejandro, 2016. "Exploring the Potential of Machine Learning for Automatic Slum Identification from VHE Imagery," Research Department working papers 975, CAF Development Bank Of Latinamerica.
    • Handle: RePEc:dbl:dblwop:975
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    File URL: https://scioteca.caf.com/handle/123456789/975
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    Cited by:

    1. Paloma Merodio Gómez & Olivia Jimena Juarez Carrillo & Monika Kuffer & Dana R. Thomson & Jose Luis Olarte Quiroz & Elio Villaseñor García & Sabine Vanhuysse & Ángela Abascal & Isaac Oluoch & Michael N, 2021. "Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images," Sustainability, MDPI, vol. 13(22), pages 1-21, November.

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    Keywords

    Ciudades; Desarrollo urbano; Economía; Equidad e inclusión social; Georreferenciación; Investigación socioeconómica; Pobreza; Políticas públicas; Servicios públicos; Vivienda;
    All these keywords.

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