Computational Infrastructure of SoilGrids 2.0

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review


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.
Original languageEnglish
Title of host publicationInternational Symposium on Environmental Software Systems (ISESS 2020)
Subtitle of host publicationEnvironmental Software Systems. Data Science in Action
Place of PublicationWageningen
ISBN (Electronic)9783030398156
ISBN (Print)9783030398149
Publication statusPublished - 29 Jan 2020

Publication series

NameIFIP Advances in Information and Communication Technology
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

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