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High-Resolution Income Estimates Using Satellite Imagery: A Deep Learning Approach applied in Buenos Aires

Author

Listed:
  • Abbate Nicolás Francisco
  • Gasparini Leonardo
  • Ronchetti Franco
  • Quiroga Facundo

Abstract

In this study, we examine the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We trained a convolutional neural network with satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census data to estimate per capita income at a 50x50 meter resolution for 2013, 2018, and 2022. This outperformed the resolution and frequency of available census information. Based on the EfficientnetV2 architecture, the model achieved high accuracy in predicting household incomes ($R^2=0.878$), surpassing the spatial resolution and model performance of other methods used in the existing literature. This approach presents new opportunities for the generation of highly disaggregated data, enabling the assessment of public policies at a local scale, providing tools for better targeting of social programs, and reducing the information gap in areas where data is not collected.

Suggested Citation

  • Abbate Nicolás Francisco & Gasparini Leonardo & Ronchetti Franco & Quiroga Facundo, 2024. "High-Resolution Income Estimates Using Satellite Imagery: A Deep Learning Approach applied in Buenos Aires," Asociación Argentina de Economía Política: Working Papers 4701, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4701
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    References listed on IDEAS

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    1. Esther Rolf & Jonathan Proctor & Tamma Carleton & Ian Bolliger & Vaishaal Shankar & Miyabi Ishihara & Benjamin Recht & Solomon Hsiang, 2021. "A generalizable and accessible approach to machine learning with global satellite imagery," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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