Service or Tool

Machine Learning Models WMS

Geoscience Australia
MEGIS (Custodian of) Manager Client Services (Point of contact)
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Licence & Rights

RESTRICTED Creative Commons Attribution 4.0 International Licence View details

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Rights Statement

Creative Commons Attribution 4.0 International Licence

Rights Statement

CC-BY

Rights Statement

4.0

Rights Statement

http://creativecommons.org/licenses/

Rights Statement

Australian Government Security Classification System

Rights Statement

https://www.protectivesecurity.gov.au/Pages/default.aspx

Licence

Creative Commons Attribution 4.0 International Licence

http://creativecommons.org/licenses/by/4.0

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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.

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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.
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153.67191,-8.9995 153.67191,-43.7605 112.89991,-43.7605 112.89991,-8.9995 153.67191,-8.9995

133.285914375,-26.380000020874

text: westlimit=112.899914375; southlimit=-43.76050004081041; eastlimit=153.671914375; northlimit=-8.999500000938234; projection=4283

Subjects
Australia | Published_External | Surface Conductivity | WMS | geoscientificInformation | grid | machine learning | potassium | radiometric | thorium | uranium | web service |

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Machine Learning Models WMS

uri : https://services.ga.gov.au/gis/machine-learning-models/wms

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