Ben Abbes A. auteur aut IRD Machicao J. auteur aut IRD Corrêa P.L.P. auteur aut IRD Specht A. auteur aut IRD Devillers Rodolphe auteur aut IRD Ometto J.P. auteur aut IRD Kondo Y. auteur aut IRD Mouillot D. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Measuring socioeconomic indices at the scale of regions or countries is required in various contexts, in particular to inform public policies. The use of Deep Learning (DL) and Earth Observation (EO) data is becoming increasingly common to estimate specific variables like societal wealth. This paper presents an end-to-end framework 'DeepWealth' that calculates such a wealth index using open-source EO data and DL. We use a multidisciplinary approach incorporating satellite imagery, socio-economic data, and DL models. We demonstrate the effectiveness and generalizability of DeepWealth by training it on 24 African countries and deploying it in Madagascar, Brazil and Japan. Our results show that DeepWealth provides accurate and stable wealth index estimates with an of 0.69. It empowers computer-literate users skilled in Python and R to estimate and visualize well-being-related data. This open-source framework follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, providing data, source code, metadata, and training checkpoints with its source code made available on Zenodo and GitHub. In this manner, we provide a DL framework that is reproducible and replicable. specialized ANGOLA BENIN BURKINA FASO CAMEROUN COTE D'IVOIRE ETHIOPIE GHANA GUINEE KENYA LESOTHO MALAWI MALI MOZAMBIQUE NIGERIA MADAGASCAR RWANDA SENEGAL SIERRA LEONE TANZANIE TOGO OUGANDA ZAMBIE ZIMBABWE 122 126 094 021 27 101785 [8 ] 2024 2352-7110 https://www.documentation.ird.fr/hor/fdi:010091575 10.1016/j.softx.2024.101785 2352-7110 [F B010091575] https://www.documentation.ird.fr/hor/fdi:010091575 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2024-08/010091575.pdf IRD - Base Horizon / Pleins textes 2024-07-24 2025-11-06 fdi:010091575 fre