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The UN-Habitat estimates that over one billion people live in slums aroundthe world. However, state-of-the-art techniques to detect the location of slumareas employ high-resolution satellite imagery, which is costly to obtain andprocess. As a result, researchers have started to look at utilising free andopen-access medium resolution satellite imagery. Yet, there is no clearconsensus on which data preparation and machine learning approaches are themost appropriate to use with such imagery data. In this paper, we evaluate twotechniques (multi-spectral data and grey-level co-occurrence matrix featureextraction) on an open-access dataset consisting of labelled Sentinel-2 imageswith a spatial resolution of 10 meters. Both techniques were paired with acanonical correlation forests classifier. The results show that the grey-levelco-occurrence matrix performed better than multi-spectral data for all fourcities. It had an average accuracy for the slum class of 97% and a meanintersection over union of 94%, while multi-spectral data had 75% and 64% forthe respective metrics. These results indicate that open-access satelliteimagery with a resolution of at least 10 meters may be suitable for keepingtrack of development goals such as the detection of slums in cities.

Mapping Slums with Medium Resolution Satellite Imagery: a Comparative Analysis of Multi-Spectral Data and Grey-level Co-occurrence Matrix Techniques