@article{duque2017exploring,
title = {Exploring the potential of machine learning for automatic slum identification from VHR imagery},
author = {Duque, Juan C. and Patino, Jorge E. and Betancourt, Alejandro},
url = {https://doi.org/10.3390/rs9090895},
doi = {10.3390/rs9090895},
issn = {2072-4292},
year = {2017},
date = {2017-08-30},
journal = {Remote Sensing},
volume = {9(9)},
number = {895},
pages = {1-23},
abstract = {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.},
keywords = {Aprendizaje de máquina, imágenes satelitales, pobreza, Sostenibilidad de ciudades},
pubstate = {published},
tppubtype = {article}
}

@article{arribas2017remote,
title = {Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning},
author = {Arribas-Bel, Daniel and Patino, Jorge E. and Duque, Juan C.},
url = {https://doi.org/10.1371/journal.pone.0176684},
doi = {10.1371/journal.pone.0176684},
issn = { 1932-6203},
year = {2017},
date = {2017-05-02},
journal = {PloS one},
volume = {12},
number = {4},
pages = {1-25},
abstract = {This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.},
keywords = {Aprendizaje de máquina, pobreza, Sostenibilidad de ciudades},
pubstate = {published},
tppubtype = {article}
}