“…Previous works have explored the use of remote sensing as a proxy for for socio-economic class differentiation within a city (Avelar et al, 2009;Tapiador et al, 2011), and several works have demonstrated the usefulness of remote sensing to distinguish a slum from its surrounding neighborhoods in the last decade (Barros, 2008;Hofmann et al, 2008;Kit et al, 2012Kit et al, , 2013Kohli et al, 2012;Netzband et al, 2009;Niebergall et al, 2007;Owen and Wong, 2013;Rhinane, 2011;Stow et al, 2007;Shekhar, 2012;Stoler et al, 2012;Weeks et al, 2007). Most previous works of slum detection from remotely sensed data have used high and very high spatial resolution (VHR) imagery from Quickbird, Ikonos, and SPOT 5 sensors, and some approaches have used complementary socio-economic data on housing size and value, while others have used only textural and fractal dimensions derived from satellite imagery to implement statistical regressions (Patino and Duque, 2013).…”