MITIGATION STRATEGIES TO IMPROVE REPRODUCIBILITY OF POVERTY ESTIMATIONS FROM REMOTE SENSING IMAGES USING DEEP LEARNING.
Machicao J, A Ben Abbes, L Meneguzzi, PLP Correa, A Specht, R David, G Subsol, D Vellenich, R Devillers, S Stall, N Mouquet, M Chaumont, L Berti-Equille, and D Mouillot (2022)
Earth and Space Science 9 10.1029/2022EA002379.
Key message : This paper aims to help researchers understand the challenges of reproducing Deep Learning (DL) publications, mitigate reproducibility gaps, and make their own work more reproducible. We build on the work of others and add recommendations organized by (a) the quality of the data set (and associated metadata), (b) the DL methodology, (c) the implementation methodology, and the infrastructure used. To our knowledge, this is the first initiative of its kind to address the problem of reproducibility in remote sensing imagery and DL problems for real-world tasks. We hope this paper lowers the barrier to entry for the DL community to improve research. Following the lifecycle mantra: reproduce!, then replicate! With the goal of improving reproducibility!
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OTHER TOPICS: Aesthetics of Biodiversity, Biodiversity & Ecosystem Functioning, Biogeography, Macroecology & Ecophylogenetics, Experimental Evolution,
Functional Biogeography, Functional Rarity, Metacommunities, Metaecosystems, Reviews and Synthesis, Trophic Biogeography & Metaweb