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

Nicolás Francisco Abbate, Leonardo Gasparini, Franco Ronchetti and Facundo Quiroga

No 4701, Asociación Argentina de Economía Política: Working Papers from Asociación Argentina de Economía Política

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.

JEL-codes: C45 C81 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2024-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:aep:anales:4701

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