Mitigation Strategies to Improve Reproducibility of Poverty Estimations From Remote Sensing Images Using Deep Learning

Jeaneth Machicao (), Imed Riadh Farah (), Leonardo Meneguzzi, Corrêa Pedro Luiz Pizzigatti (), Alison Specht (), Romain David (), Gérard Subsol (), Danton Ferreira Vellenich, Rodolphe Devillers (), Shelley Stall, Nicolas Mouquet (), Marc Chaumont (), Laure Berti-Équille () and David Mouillot ()
Additional contact information
Jeaneth Machicao: EPUSP - Departamento de Engenharia da Produção [São Paulo] - Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]
Imed Riadh Farah: FRB - Fondation pour la recherche sur la Biodiversité, UMA - Université de la Manouba [Tunisie]
Leonardo Meneguzzi: EPUSP - Departamento de Engenharia da Produção [São Paulo] - Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]
Corrêa Pedro Luiz Pizzigatti: EPUSP - Departamento de Engenharia da Produção [São Paulo] - Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]
Alison Specht: UQ [All campuses : Brisbane, Dutton Park Gatton, Herston, St Lucia and other locations] - The University of Queensland
Romain David: ERINHA-AISBL - European Research Infrastructure on Highly Pathogenic Agents
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
Danton Ferreira Vellenich: EPUSP - Departamento de Engenharia da Produção [São Paulo] - Escola Politecnica da Universidade de Sao Paulo [Sao Paulo]
Rodolphe Devillers: 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
Shelley Stall: American Geophysical Union [Washington]
Nicolas Mouquet: FRB - Fondation pour la recherche sur la Biodiversité, UNIMES - Université de Nîmes
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
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
David Mouillot: UMR MARBEC PALAVAS - MARine Biodiversity Exploitation and Conservation - Station Ifremer Palavas - UMR MARBEC - MARine Biodiversity Exploitation and Conservation - MARBEC - IRD - Institut de Recherche pour le Développement - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - CNRS - Centre National de la Recherche Scientifique - UM - Université de Montpellier, UM - Université de Montpellier

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Abstract: The challenges of Reproducibility and Replicability (R & R) in computer science experiments have become a focus of attention in the last decade, as efforts to adhere to good research practices have increased. However, experiments using Deep Learning (DL) remain difficult to reproduce due to the complexity of the techniques used. Challenges such as estimating poverty indicators (e.g. wealth index levels) from remote sensing imagery, requiring the use of huge volumes of data across different geographic locations, would be impossible without the use of DL technology. To test the reproducibility of DL experiments, we report a review of the reproducibility of three DL experiments which analyse visual indicators from satellite and street imagery. For each experiment, we identify the challenges found in the datasets, methods and workflows used. As a result of this assessment we propose a checklist incorporating relevant FAIR principles to screen an experiment for its reproducibility. Based on the lessons learned from this study, we recommend a set of actions aimed to improve the reproducibility of such experiments and reduce the likelihood of wasted effort. We believe that the target audience is broad, from researchers seeking to reproduce an experiment, authors reporting an experiment, or reviewers seeking to assess the work of others.

Keywords: Reproducibility; Replicability; Deep learning; Machine learning; FAIR; poverty indicators (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp and nep-exp
Note: View the original document on HAL open archive server: https://hal.science/hal-03761874v1
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Published in Earth and Space Science, 2022, 9 (8), pp.e2022EA002379. ⟨10.1029/2022ea002379⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03761874

DOI: 10.1029/2022ea002379

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