Authors / CoAuthors
Abstract
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).
Product Type
service
eCat Id
146040
Contact for the resource
Keywords
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- radiometricmachine learninggridpotassiumthoriumuraniumAustraliaweb serviceWCS
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- Published_External
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- Surface Conductivity
Publication Date
Creation Date
Security Constraints
Legal Constraints
Status
Purpose
Maintenance Information
Topic Category
Series Information
Lineage
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.
Parent Information
Extents
Reference System
4283
Spatial Resolution
Service Information
Type - OGC:WCS
Version - 1.1.1 1.0.0 2.0.1
Coupled Resource -
Connect Point - https://services.ga.gov.au/gis/machine-learning-models/wcs?REQUEST=GetCapabilities&SERVICE=WCS
Associations
Association Type - operatesOn
Predictive grids of major oxide concentrations in surface rock and regolith over the Australian continent
eCat Identifier - 148587,
UUID - 5ea9a9e9-6253-464f-8aed-0c19124ee910
Association Type - operatesOn
National surface and near-surface conductivity grids
eCat Identifier - 148588,
UUID - 6ffc7fe1-825c-4bf7-9911-35f55b98a36c
Source Information