From Geoscience Australia
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Machine Learning Models WMS

Created 09/01/2026

Updated 09/01/2026

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This web service contains map layers and coverages for machine learning models, using raster datasets which include radiometric grid infill, cover depths and conductivity. All grids have been converted to cloud-optimised GeoTIFF (COG) format for use and delivery from an cloud-based object store (AWS s3). For potassium (K), thorium (Th) and uranium (U) radiometric infill grids, an equalised histogram was applied to each grid. The radiometric ternary image has no style applied, with from transparency for no-data values. A tile service (WMTS) is also integrated into the WMS to provide a high-performing service for integration into web maps and online mapping portals.

Files and APIs

Tags

Additional Info

Field Value
Title Machine Learning Models WMS
Language English
Licence Not Specified
Landing Page https://data.gov.au/data/dataset/37c73aab-fb43-4c77-86c6-93337c3a5d39
Contact Point
Geoscience Australia Data
clientservices@ga.gov.au
Reference Period 05/08/2022
Geospatial Coverage
Map data © OpenStreetMap contributors
{
  "coordinates": [
    [
      [
        112.899914375,
        -43.76050004081041
      ],
      [
        153.671914375,
        -43.76050004081041
      ],
      [
        153.671914375,
        -8.999500000938234
      ],
      [
        112.899914375,
        -8.999500000938234
      ],
      [
        112.899914375,
        -43.76050004081041
      ]
    ]
  ],
  "type": "Polygon"
}
Data Portal Geoscience Australia

Data Source

This dataset was originally found on Geoscience Australia "Machine Learning Models WMS". Please visit the source to access the original metadata of the dataset:
https://ecat.ga.gov.au/geonetwork/srv/eng/csw/dataset/machine-learning-models-wms