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

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 MobileNetV3 architecture, the model achieved high accuracy in predicting household incomes (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.77), 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.