  Urbanisation is a great challenge for modern societies, promising better
access to economic opportunities while widening socioeconomic inequalities.
Accurately tracking how this process unfolds has been challenging for
traditional data collection methods, while remote sensing information offers an
alternative to gather a more complete view on these societal changes. By
feeding a neural network with satellite images one may recover the
socioeconomic information associated to that area, however these models lack to
explain how visual features contained in a sample, trigger a given prediction.
Here we close this gap by predicting socioeconomic status across France from
aerial images and interpreting class activation mappings in terms of urban
topology. We show that the model disregards the spatial correlations existing
between urban class and socioeconomic status to derive its predictions. These
results pave the way to build interpretable models, which may help to better
track and understand urbanisation and its consequences.
