loading page

Deep learning methodology for predicting socioeconomic indicators in Vale do Ribeira using satellite imagery
  • Isabella de Melo Sousa,
  • M. Jeaneth Machicao Justo,
  • Pedro Corrêa
Isabella de Melo Sousa
Escola Politécnica da Universidade de São Paulo

Corresponding Author:melosousa.isabella@gmail.com

Author Profile
M. Jeaneth Machicao Justo
Universidade de São Paulo
Author Profile
Pedro Corrêa
Universidade de São Paulo
Author Profile


Key measures of socioeconomic indicators are essential for making informed policy decisions, but due to the high costs and operational difficulties of traditional data collection efforts, obtaining reliable socioeconomic data remains a challenge, particularly in developing countries. This work presents a deep learning methodology to estimate socioeconomic indicators using satellite imagery. The neural network model developed was trained at the Brazilian region of Vale do Ribeira with the goal of analyzing the socioeconomic indicator of income. The preliminary results showed that models using nightlight (NL) or multispectral daytime (MS) imagery performed better than models trained only on RGB bands and that models trained exclusively on NL or MS imagery performed similar to one another and nearly as well as the combined model MS+NL. Finally, the model yielded a low performance (R2 = 0.289), but it is still promising once the dataset employed was considerably smaller than the one used in the original study that attempted to replicate.