Jarry R., Chaumont M., Berti-Equille Laure, Subsol G. (2023). Comparing spatial and spatio-temporal paradigms to estimate the evolution of socio-economical indicators from satellite images. In :
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium : proceedings. Piscataway : IEEE, 5790-5793. IGARSS. International Geoscience and Remote Sensing Symposium, Pasadena (USA), 2023/07/16-21. ISBN 979-8-3503-2010-7.
Titre du document
Comparing spatial and spatio-temporal paradigms to estimate the evolution of socio-economical indicators from satellite images
Année de publication
2023
Type de document
Partie d'ouvrage
Auteurs
Jarry R., Chaumont M., Berti-Equille Laure, Subsol G.
In
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium : proceedings
Source
Piscataway : IEEE, 2023,
5790-5793 ISBN 979-8-3503-2010-7
Colloque
IGARSS. International Geoscience and Remote Sensing Symposium, Pasadena (USA), 2023/07/16-21
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.
