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

Robin Jarry (), Marc Chaumont (), Laure Berti-Equille () and Gérard Subsol ()
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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: UNIMES - Université de Nîmes, 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

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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.

Keywords: Remote Sensing; Image Time Series; Deep Learning; Transformer; Socio-economic indicator (search for similar items in EconPapers)
Date: 2024-11-25
New Economics Papers: this item is included in nep-cmp and nep-env
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Published in MVEO 2024 - Workshop on Machine Vision for Earth Observation and Environment Monitoring, Nov 2024, Glasgow, United Kingdom

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