Satellite images and deep learning for the prediction of socioeconomic indicators in the Vale do Ribeira

  • Jeaneth Machicao USP
  • Iago Fava da Costa USP
  • Enrico Triñanes USP
  • Pedro Corrêa USP


Due to low awareness and low investment in data collection and processing in developing countries, industry has struggled to plan and collaborate with public policy on data-driven socioeconomic decisions. This paper presents a deep learning approach to estimate socioeconomic indicators using satellite imagery. For this purpose, a region in southeastern Brazil called Vale do Ribeira was selected as a study case due to its data availability and environmental relevance. We used publicly available data to analyze three socioeconomic indicators: Longevity, Income and Literacy, the main representatives of the Human Development Index. Our preliminary results show a relevant correlation between satellite imagery and the income indicator, although they are not yet conclusive for longevity and literacy.

Palavras-chave: deep learning, remote sensing imagery, poverty estimation


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MACHICAO, Jeaneth; COSTA, Iago Fava da; TRIÑANES, Enrico; CORRÊA, Pedro. Satellite images and deep learning for the prediction of socioeconomic indicators in the Vale do Ribeira. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 16. , 2022, Niterói. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 9-16. ISSN 2763-8774. DOI: