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Communication Dans Un Congrès Année : 2023

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

Robin Jarry
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Marc Chaumont
Gérard Subsol

Résumé

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.
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Dates et versions

hal-04268542 , version 1 (10-01-2024)

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Robin Jarry, Marc Chaumont, Laure Berti-Équille, Gérard Subsol. Comparing spatial and spatio-temporal paradigms to estimate the evolution of socio-economical indicators from satellite images. 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⟩. ⟨hal-04268542⟩
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