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

## TL;DR

This paper shows how satellite images and deep learning can create detailed income maps, helping policymakers in Buenos Aires and beyond.

## Contribution

The study introduces a high-resolution income estimation model using satellite imagery and machine learning.

## Key findings

- The model achieved an R2 score of 0.878 in predicting household incomes in Buenos Aires.
- The spatial resolution of the model is over 20 times finer than existing methods.
- The approach can generate income maps for any time period using arbitrary satellite images.

## 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 finer than existing methods in the literature. The model also allows estimating income maps for arbitrary images, and can therefore be applied at any point in time. Our approach opens up new possibilities for generating highly detailed data, which can be used to assess public policies at a local level, target social programs more effectively, and address information gaps in areas where traditional data collection methods are lacking.