Product catalogue
Machine Learning Models WMS
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.
Simple
Identification info
- Date (Creation)
- 2021-10-27T00:00:00
- Date (Publication)
- 2021-12-09T06:38:25
- Date (Revision)
- 2022-08-05
- Point of contact
-
Role Organisation / Individual Name Details Custodian MEGIS
1
- Topic category
-
- Geoscientific information
Extent
))
- Maintenance and update frequency
- As needed
- Keywords
-
-
radiometric
-
machine learning
-
grid
-
potassium
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thorium
-
uranium
-
Australia
-
web service
-
WMS
-
- Keywords
-
-
Published_External
-
- Keywords
-
-
Surface Conductivity
-
Resource constraints
- Title
-
Creative Commons Attribution 4.0 International Licence
- Alternate title
-
CC-BY
- Edition
-
4.0
- Access constraints
- License
- Use constraints
- License
Resource constraints
- Title
-
Australian Government Security Classification System
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Associated resource
- Association Type
- Operates on
- Title
-
Predictive grids of major oxide concentrations in surface rock and regolith over the Australian continent
- Citation identifier
- 148587
- Citation identifier
- 5ea9a9e9-6253-464f-8aed-0c19124ee910
Associated resource
- Association Type
- Operates on
- Title
-
National surface and near-surface conductivity grids
- Citation identifier
- 148588
- Citation identifier
- 6ffc7fe1-825c-4bf7-9911-35f55b98a36c
- Service Version
-
1.3.0
- Service Version
-
1.1.1
- Coupling Type
- Tight
Contains Operations
- Operation Name
-
GetCapabilities
- Distributed computing platform (DCP)
- WebServices
- Operation Description
-
The GetCapabilities operation is used to obtain service metadata, which is a machine-readable (and human-readable) description of the server's information content and acceptable request parameter values.
- Connect Point
-
https://services.ga.gov.au/gis/machine-learning-models/wms?REQUEST=GetCapabilities&SERVICE=WMS
- Name
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SERVICE
- Type name
-
TEXT
- Parameter direction
-
- Description
-
The mandatory SERVICE parameter indicates which of the available service types at a particular server is being invoked. When invoking GetCapabilities on a WFS the value WFS shall be used.
- Optionality
- false
- Repeatability
- false
- Name
-
REQUEST
- Type name
-
TEXT
- Parameter direction
-
- Description
-
The mandatory REQUEST parameter indicates which service operation is being invoked. To invoke the GetCapabilities operation, the value GetCapabilities shall be used.
- Optionality
- false
- Repeatability
- false
- Name
-
VERSION
- Type name
-
TEXT
- Parameter direction
-
- Description
-
The optional VERSION parameter indicates the service type version number to use. In response to a GetCapabilities request that does not specify a version number, the server shall respond with the highest version it supports.
- Optionality
- true
- Repeatability
- false
Distribution Information
- Distributor contact
-
Role Organisation / Individual Name Details Distributor Geoscience Australia
Voice
- OnLine resource
-
Machine Learning Models WMS
Machine Learning Models WMS
- Distribution format
-
-
OGC:WMS
-
Resource 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.
- Hierarchy level
- Service
Reference System Information
- Reference system identifier
-
EPSG/4283
- Title
-
European Petroleum Survey Group (EPSG) Geodetic Parameter Registry
- Date (Publication)
- 2008-11-12T00:00:00
- Cited responsible party
-
Role Organisation / Individual Name Details European Petroleum Survey Group
http://www.epsg-registry.org/
Metadata constraints
- Title
-
Australian Government Security Classification System
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Metadata
- Metadata identifier
-
urn:uuid/958f3685-d7c8-4874-8601-8a4bfd84c676
- Title
-
GeoNetwork UUID
- Contact
-
Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice
Type of resource
- Resource scope
- Service
- Name
-
service
Alternative metadata reference
- Title
-
Geoscience Australia - short identifier for metadata record with
uuid
- Citation identifier
- eCatId/146039
- Date info (Revision)
- 2022-08-05T02:39:22
- Date info (Creation)
- 2021-10-27T03:47:13
Metadata standard
- Title
-
AU/NZS ISO 19115-1:2014
Metadata standard
- Title
-
ISO 19115-1:2014
Metadata standard
- Title
-
ISO 19115-3 (Draft Schemas 2015)
- Edition date
- 2015-07-01T00:00:00
- Title
-
Geoscience Australia Community Metadata Profile of ISO 19115-1:2014
- Edition
-
Version 2.0, April 2015