  The UN-Habitat estimates that over one billion people live in slums around
the world. However, state-of-the-art techniques to detect the location of slum
areas employ high-resolution satellite imagery, which is costly to obtain and
process. As a result, researchers have started to look at utilising free and
open-access medium resolution satellite imagery. Yet, there is no clear
consensus on which data preparation and machine learning approaches are the
most appropriate to use with such imagery data. In this paper, we evaluate two
techniques (multi-spectral data and grey-level co-occurrence matrix feature
extraction) on an open-access dataset consisting of labelled Sentinel-2 images
with a spatial resolution of 10 meters. Both techniques were paired with a
canonical correlation forests classifier. The results show that the grey-level
co-occurrence matrix performed better than multi-spectral data for all four
cities. It had an average accuracy for the slum class of 97% and a mean
intersection over union of 94%, while multi-spectral data had 75% and 64% for
the respective metrics. These results indicate that open-access satellite
imagery with a resolution of at least 10 meters may be suitable for keeping
track of development goals such as the detection of slums in cities.
