Computational Infrastructure of SoilGrids 2.0 - Environmental Software Systems. Data Science in Action Access content directly
Conference Papers Year : 2020

Computational Infrastructure of SoilGrids 2.0


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.
Fichier principal
Vignette du fichier
493342_1_En_3_Chapter.pdf (329.43 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03361904 , version 1 (01-10-2021)





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⟩
46 View
61 Download



Gmail Facebook X LinkedIn More