Deep learning with satellite images enables high-resolution income estimation: A case study of Buenos Aires.

ABSTRACT: High-resolution income data is crucial for informing policy decisions as it allows policymakers to better understand the distribution of wealth and poverty. However, obtaining this information is often cost-prohibitive, especially in developing countries. We evaluate the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We train a 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. The model, based on the EfficientNetV2 architecture, demonstrates strong predictive accuracy for household incomes (R2 = 0.878), achieving a spatial resolution over 20 times

SUBMITTER: Abbate NF 

PROVIDER: S-EPMC12810835 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature