Deep Learning with Satellite Images Enables High-Resolution Income Estimation: a Case Study of Buenos Aires

This repository contains three datasets generated from a machine learning model applied to high-resolution satellite imagery. The dataset includes per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (Rsquared=0.878), surpassing existing methods in spatial resolution and performance. 

Datasets are published in geoparquet format. Since the predictions for each cell individually present some random variation, we recommend that the results are used averaging out the estimations for each area of interest (municipalities, neighborhoods, sections or census tracts) and not at an individual level. As we detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict in a precise way the households’ income.

Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images.

Data can be visualized by accessing: https://ingresoamba.netlify.app