Brief description
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.Lineage
Statement: Service generated using supplied raster grids, which have been transformed into the cloud-optimised GeoTIFF (COG) format for use in a cloud object store (AWS s3). All grids were transformed using GDAL, with the cog option as the output format. Raster layers added August 2022 for surface conductivity models generated from a machine learning covariate prediction approach based on the Northern Australian regional Aus-AEM survey dataset.text: westlimit=112.899914375; southlimit=-43.76050004081041; eastlimit=153.671914375; northlimit=-8.999500000938234; projection=4283
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Machine Learning Models WMS
uri :
https://services.ga.gov.au/gis/machine-learning-models/wms
- URI : services.ga.gov.au/gis/machine-learning-models/wms
- global : 958f3685-d7c8-4874-8601-8a4bfd84c676