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

Documentos de trabalho de investigação socioeconómica

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

Data de publicação: 2016-11-23

Autores: Betancourt, Alejandro ; Duque, Juan Carlos ; Patino, Jorge Eduardo

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.

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Ficha técnica

Idioma: en

País / Região: Argentina, Brasil, Colombia

Formato: pdf

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Citar publicação

Betancourt, Alejandro; Duque, Juan Carlos; Patino, Jorge Eduardo. (2016). Exploring the Potential of Machine Learning for Automatic Slum Identification from VHE Imagery. Buenos Aires: CAF

Autores e autoras

Betancourt, Alejandro

Número de publicações 1

Duque, Juan Carlos

Número de publicações 2

Patino, Jorge Eduardo

Número de publicações 1

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