Published June 30, 2024 | Version V2
Dataset Open

Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale

  • 1. OpenGeoHub
  • 2. OpenGeoHub foundation

Description

Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale produced by GEOZS, Slovenia. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and INSPIRE Lithology and Geochronologic Era URI codelists. Layers include:

  • EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map;
  • dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map;

Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map.

Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.

Files

00_surface.lithology.png

Additional details

Related works

Is referenced by
Dataset: 10.5281/zenodo.4056634 (DOI)

Funding

European Commission
AI4SoilHealth – AI4SoilHealth: Accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory 101086179