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Computational Infrastructure of SoilGrids 2.0

Abstract : SoilGrids maps soil properties for the entire globe at medium spatial resolution (250 m cell side) using state-of-the-art machine learning methods. The expanding pool of input data and the increasing computational demands of predictive models required a prediction framework that could deal with large data. This article describes the mechanisms set in place for a geo-spatially parallelised prediction system for soil properties. The features provided by GRASS GIS – mapset and region – are used to limit predictions to a specific geographic area, enabling parallelisation. The Slurm job scheduler is used to deploy predictions in a high-performance computing cluster. The framework presented can be seamlessly applied to most other geo-spatial process requiring parallelisation. This framework can also be employed with a different job scheduler, GRASS GIS being the main requirement and engine.
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Submitted on : Friday, October 1, 2021 - 3:41:45 PM
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Luís Sousa, Laura Poggio, Gwen Dawes, Bas Kempen, Rik van Den Bosch. Computational Infrastructure of SoilGrids 2.0. 13th International Symposium on Environmental Software Systems (ISESS), Feb 2020, Wageningen, Netherlands. pp.24-31, ⟨10.1007/978-3-030-39815-6_3⟩. ⟨hal-03361904⟩



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