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

Robin Jarry (), Marc Chaumont (), Laure Berti-Équille () 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: 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

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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.

Keywords: Zanzibar; Tanzania; Deep learning; Time series analysis; Estimation; Predictive models; Satellite images; Standards; Remote sensing (search for similar items in EconPapers)
Date: 2023-07-16
New Economics Papers: this item is included in nep-big and nep-ure
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Published in IGARSS 2023 - IEEE International Geoscience and Remote Sensing Symposium, Sidharth Misra; Shannon Brown, Jul 2023, Pasadena, CA, United States. pp.5790-5793, ⟨10.1109/IGARSS52108.2023.10282306⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04268542

DOI: 10.1109/IGARSS52108.2023.10282306

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