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Urbanisation is a great challenge for modern societies, promising betteraccess to economic opportunities while widening socioeconomic inequalities.Accurately tracking how this process unfolds has been challenging fortraditional data collection methods, while remote sensing information offers analternative to gather a more complete view on these societal changes. Byfeeding a neural network with satellite images one may recover thesocioeconomic information associated to that area, however these models lack toexplain how visual features contained in a sample, trigger a given prediction.Here we close this gap by predicting socioeconomic status across France fromaerial images and interpreting class activation mappings in terms of urbantopology. We show that the model disregards the spatial correlations existingbetween urban class and socioeconomic status to derive its predictions. Theseresults pave the way to build interpretable models, which may help to bettertrack and understand urbanisation and its consequences.

Socioeconomic correlations of urban patterns inferred from aerial images: interpreting activation maps of Convolutional Neural Networks