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Estimation of socioeconomic indicators through satellite imagery in the NEXUS area

Domaine:

socioeconomicgeospatial

Type de record:

paper
Créateur:
Carlos Massao Oishi GiuzioIgor Varela ZoellerRafael Araujo Coelho
Éditeur:
Jeaneth MachicaoPedro Luiz Pizzigatti CorreaPedro Ribeiro de Andrade Neto
Éditeur:
Zenodo
Hôte:avatar

Undergraduate monograph

Abstract:

Parsec project aims to apply Data Science and Machine Learning techniques to applications for management and preservation of the world's biodiversity. In partnership with the University of São Paulo and the Brazilian National Institute for Space Research, INPE, a study is being conducted in the NEXUS area, a region of approximately 3.4 million square kilometers within the Brazilian territory, covering the São Francisco and Parnaíba River basins.This graduation project aids that study by proposing a methodology inspired by work done in the African continent in previous years, to estimate income, literacy and longevity indicators across this area, using publicly available satellite images collected with Google Earth Engine API. The contribution of this work is the creation and step-by-step documentation of a methodology for estimating indicators in the Brazilian territory through the training of Deep Learning models using satellite imagery, in addition to proposing a prototype of an interactive internet platform to the visualization of estimated indicators in the Brazilian map. The work also contributes with the creation of a new dataset, originated from merging a set containing more than 100 socioeconomic indicators provided by INPE and the administrative, geographic and geometric information of the Brazilian census sectors provided by IBGE. Deep Learning predictive models were trained using the backbone of a ResNet-18 to estimate socioeconomic indicators using multispectral and nightlight images. It was observed that multispectral experiments were appropriate for predicting the target values over the proposed dataset, while models using only nightlights performed below expectations due to data limitations. In view of the results obtained, it is understood that the methodology proposed by this work is suitable for the estimation of socioeconomic indicators in the Brazilian scenario.

via: pcs.usp.br

 

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

Visit

doi.org

Tasks

computer vision

Tags

deep learningremote sensingpoverty estimationBrazil area

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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