 This paper reviews the current state of remote sensing research related to slum mapping. It finds that there is a lack of understanding of the contextual factors of slums, as well as a need for better physical slum characterization. Additionally, the use of high-resolution images presents challenges when it comes to extracting accurate information from slums. Finally, the paper suggests that a combination of techniques, including texture-based methods, machine learning algorithms, and object-based image analysis, could be used to accurately map slums at different scales. This article was authored by Monica Cuffer, Karen Pfeffer, and Richard Slyeuses.