Abstract: 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 Sao Paulo and Parana with the goal of analyzing the socioeconomic indicator of income in the Vale do Ribeira region. The model yielded a R-squared of 0.4016 and performed significantly better than the model trained only on RGB bands. via: pcs.usp.br

Acknowledgments: The PARSEC project is funded by the Belmont Forum, Collaborative Research Action on Science-Driven e-Infrastructures Innovation.