Interpretable socioeconomic status inference from aerial imagery through urban patterns

Jacob Levy Abitbol*, Márton Karsai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

Urbanization is a great challenge for modern societies, promising better access to economic opportunities, but widening socioeconomic inequalities. Accurately tracking this process as it unfolds has been challenging for traditional data collection methods, but remote sensing information offers an alternative way to gather a more complete view of these societal changes. By feeding neural networks with satellite images, the socioeconomic information associated with that area can be recovered. However, these models lack the ability 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 trained models disregard the spatial correlations existing between urban class and socioeconomic status to derive their predictions. These results pave the way to build more interpretable models, which may help to better track and understand urbanization and its consequences.

Original languageEnglish
Pages (from-to)684-692
Number of pages9
JournalNature Machine Intelligence
Volume2
Issue number11
DOIs
StatePublished - Nov 2020

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