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Predicting Socio-economic Indicator Variations with Satellite Image Time Series and Transformer

Author

Listed:
  • 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

    (UNIMES - Nîmes Université, 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)

  • Laure Berti-Equille

    (IRD - Institut de Recherche pour le Développement, 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)

  • 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

Monitoring local socio-economic variations is essential for tracking progress toward sustainable development goals. However, measuring these variations can be challenging, as it requires data collection at least twice, which is both expensive and time-consuming. To address this issue, researchers have proposed remote sensing and deep learning methods to predict socio-economic indicators. However, subtracting two predicted socio-economic indicators from different dates leads to inaccurate results. We propose a novel method for predicting socio-economic variations using satellite image time series to achieve more reliable predictions. Our method leverages both spatial and temporal information to enhance the final prediction. In our experiments, we observed that it outperforms state-of-the-art methods.

Suggested Citation

  • Robin Jarry & Marc Chaumont & Laure Berti-Equille & Gérard Subsol, 2024. "Predicting Socio-economic Indicator Variations with Satellite Image Time Series and Transformer," Post-Print lirmm-04895134, HAL.
  • Handle: RePEc:hal:journl:lirmm-04895134
    Note: View the original document on HAL open archive server: https://hal-lirmm.ccsd.cnrs.fr/lirmm-04895134v2
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    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Remote Sensing; Image Time Series; Deep Learning; Transformer; Socio-economic indicator;
    All these keywords.

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