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Interpretable socioeconomic status inference from aerial imagery through urban patterns

A preprint version of the article is available at arXiv.


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.

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Fig. 1: Sample of overlaid datasets (Paris).
Fig. 2: Observed performance of models trained to predict wealth in French cities: confusion matrices between predicted and observed SES classes.
Fig. 3: Maps of observed and predicted average income for Paris.
Fig. 4: Model interpretability studies using guided Grad-CAM.
Fig. 5: Correlations between urban topology and SES in the city of Paris.
Fig. 6: Heatmap representation of low/high SES coactivation and co-appearance gain values for the city of Paris.

Data availability

In this paper we built on the combination of three publicly available datasets. One was issued by the National Geographical Information Institute (IGN) and contains aerial images about French municipalities20. The second was provided by the French National Institute of Statistics and Economic Studies (INSEE) in 201921 and provides a high-resolution socioeconomic map. The third was shared by the European Environment Agency through the 2012 European Union Urban Atlas project about EU28 and EFTA countries22. All datasets are public and openly accessible online at, and The individual files that were downloaded from the aerial imagery dataset are provided in GitHub. Figures depicting raw data are shown in Figs. 1, 3 and 4 and Supplementary Fig. 1. All figures and tables are provided as source data at

Code availability

Code developed for the research has been made freely available for non-commercial use under an MIT Licence and shared through an open repository32 at For any further questions please contact the corresponding authors.


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This work was partially funded by the SoSweet ANR project (ANR-15-CE38-0011), the MOTIf Stic-AmSud project (18-STIC-07) and the ACADEMICS project financed by IDEX LYON. M.K. was supported by the DataRedux (ANR-19-CE46-0008) ANR and the SoBigData++ (871042) H2020 projects.

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Authors and Affiliations



J.L.A. and M.K. designed the research. J.L.A. built the combined dataset and implemented analysis of the results. J.L.A. and M.K. wrote the final manuscript.

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Correspondence to Jacob Levy Abitbol or Márton Karsai.

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Extended data

Extended Data Fig. 1 Correlations between urban topology and socioeconomic status in the city of Marseille.

a, Mean model activation rate per urban class with error bars denoting the 95% confidence interval for samples predicted as respectively as low SES (blue) or high SES (red) by the model. The yellow line on the left plot indicates the random diffusion value (H0). b, Estimated probability of an urban polygon belonging to the bottom or top quintile of the income distribution with error bars denoting the 95% confidence interval. Sample sizes are provided in Supplementary Tables 2 and 3.

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Supplementary Information

Supplementary Figs. 1–13 and Tables 1–3.

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Abitbol, J.L., Karsai, M. Interpretable socioeconomic status inference from aerial imagery through urban patterns. Nat Mach Intell 2, 684–692 (2020).

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