Computational Infrastructure of SoilGrids 2.0

  • Luís M. de SousaEmail author
  • Laura Poggio
  • Gwen Dawes
  • Bas Kempen
  • Rik van den Bosch
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 554)


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.


Digital Soil Mapping High-performance computing GRASS GIS 


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Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.ISRIC - World Soil InformationWageningenThe Netherlands
  2. 2.FB-ITWageningen University and ResearchWageningenThe Netherlands

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