Published January 27, 2024
| Version v1
Dataset
Open
Dataset - DeepWealth: A Generalizable Open-Source Deep Learning Framework using Satellite Images for Well-Being Estimation
Authors/Creators
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1.
Université Montpellier 1
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2.
Universidade de São Paulo
- 3. Terrestrial Ecosystem Research Network
- 4. Institut de Recherche pour le Développement
- 5. National Institute for Space Research
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6.
Research Institute for Humanity and Nature
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7.
Marine Biodiversity Exploitation and Conservation
Description
This dataset encapsulates the Checkpoints obtained during the training process of the Deep Learning model, which can be used for new estimations.
The aim of the DeepWealth package is to provide a generalizable Deep Learning framework for the use of remote sensing in poverty estimation. The combination of Deep Learning and Earth Observation data is increasingly being used to estimate socioeconomic conditions at regional and global scales. The proposed framework aligns with the Sustainable Development Goal SDG1 of ending poverty. The framework provides open-source data, code, and training models (checkpoints) for reproducibility and replicability.
- The source code can be found in https://github.com/PARSECworld/DeepWealth
- The metadata from source code can be found in https://github.com/PARSECworld/DeepWealth/blob/main/metadata.pdf
- The paper describing the development of this framework can be found at: Ben Abbes, A., Machicao, J., Corrêa, P. L. P., Specht, A., Devillers, R., Ometto, J. P., Kondo, Y., & Mouillot, D. (2024). DeepWealth: A generalizable open-source deep learning framework using satellite images for well-being estimation. SoftwareX, 27, 101785. https://doi.org/10.1016/j.softx.2024.101785
Files
Baselines.zip
Files
(651.4 MB)
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md5:0ecc2492e63c812828d0469cd3e804aa
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md5:14e975106e8d5fa88b244f78652d84e3
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md5:24da40c041e89f21dc9e5ac339f11a19
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md5:fe1e45d8efa7c17d2c4454dedb04f1f5
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9.2 MB | Download |
Additional details
Related works
- Is part of
- Publication: 10.1016/j.softx.2024.101785 (DOI)
Funding
- U.S. National Science Foundation
- Belmont Forum Collaborative Research: Building New Tools for Data Sharing and Re-use through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas (PARSEC) 1929464
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Transição para sustentabilidade e o nexo água-agricultura-energia: explorando uma abordagem integradora com casos de estudo nos biomas Cerrado e Caatinga 2017/22269-2
- Fondation Pour la Recherche Sur la Biodiversité
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Building new tools for data sharing and re-use through a transnational investigation of the socioeconomic impacts of protected areas (PARSEC) 2018/24017–3
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Evaluation the effects of Brazilian protected areas in local communities based on the use and re-use of biological, environmental and socioeconomic data 2020/03514–9
Dates
- Submitted
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2024-02