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Comparing spatial and spatio-temporal paradigms to estimate the evolution of socio-economical indicators from satellite images

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  • Robin Jarry

    (LIRMM | ICAR - Image & Interaction - LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier)

  • Marc Chaumont

    (LIRMM | ICAR - Image & Interaction - LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier, UNIMES - Université de Nîmes)

  • Laure Berti-Équille

    (UMR 228 Espace-Dev, Espace pour le développement - IRD - Institut de Recherche pour le Développement - UPVD - Université de Perpignan Via Domitia - AU - Avignon Université - UR - Université de La Réunion - UNC - Université de la Nouvelle-Calédonie - UG - Université de Guyane - UA - Université des Antilles - UM - Université de Montpellier, AMU - Aix Marseille Université)

  • Gérard Subsol

    (LIRMM | ICAR - Image & Interaction - LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier)

Abstract

In remote sensing, deep spatio-temporal models, i.e., deep learning models that estimate information based on Satellite Image Time Series obtain successful results in Land Use/Land Cover classification or change detection. Nevertheless, for socioeconomic applications such as poverty estimation, only deep spatial models have been proposed. In this paper, we propose a test-bed to compare spatial and spatio-temporal paradigms to estimate the evolution of Nighttime Light (NTL), a standard proxy for socioeconomic indicators. We applied the test-bed in the area of Zanzibar, Tanzania for 21 years. We observe that (1) both models obtain roughly equivalent performances when predicting the NTL value at a given time, but (2) the spatio-temporal model is significantly more efficient when predicting the NTL evolution.

Suggested Citation

  • Robin Jarry & Marc Chaumont & Laure Berti-Équille & Gérard Subsol, 2023. "Comparing spatial and spatio-temporal paradigms to estimate the evolution of socio-economical indicators from satellite images," Post-Print hal-04268542, HAL.
    • Handle: RePEc:hal:journl:hal-04268542
    DOI: 10.1109/IGARSS52108.2023.10282306
    Note: View the original document on HAL open archive server: https://hal.science/hal-04268542
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    References listed on IDEAS

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    1. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    More about this item

    Keywords

    Zanzibar; Tanzania; Deep learning; Time series analysis; Estimation; Predictive models; Satellite images; Standards; Remote sensing;
    All these keywords.

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