Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery
Archivos
Fecha
2017-09-01
Autores
Duque JC
Patiño, Jorge
Betancourt, Alejandro
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI AG
Resumen
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.