37c73aab-fb43-4c77-86c6-93337c3a5d39 urn:uuid en UTF-8 nonGeographicDataset nonGeographicDataset 2026-01-08T13:42:02 creation ISO 19115 2016 Machine Learning Models WMS 37c73aab-fb43-4c77-86c6-93337c3a5d39 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. {"type": "Polygon", "coordinates": [[[112.899914375, -43.76050004081041], [153.671914375, -43.76050004081041], [153.671914375, -8.999500000938234], [112.899914375, -8.999500000938234], [112.899914375, -43.76050004081041]]]} 112.899914375 153.671914375 -43.76050004081041 -8.999500000938234 Australia Published_External Surface Conductivity WMS grid machine learning potassium radiometric thorium uranium web service Machine Learning Models WMS documentDigital Machine Learning Models WMS WMS Unnamed resource documentDigital WMS