This model is derived from geological, geophysical and other forms of geodata. Feature extraction used deep learning. Predictive modelling made use of the deep ensemble method. Displayed is a Pan-Canadian probability map of mineral potential of graphite. This map was generated using known graphite deposits and occurrences and their associated features. Higher probability values highlight areas with an increased probability of graphite mineral systems.

This model is derived from geological and geophysical data, which is processed using deep learning and natural language processing techniques. Displayed is a Pan-Canadian probability map indicating the likelihood of discovering next-generation lithium-cesium-tantalum (LCT) pegmatites. This map was generated using known Canadian LCT pegmatites and their associated geospatial features, incorporating geological and geophysical data analyzed through deep learning and natural language processing techniques. Higher probability values highlight areas with an increased likelihood of hosting next-generation deposits, making this map a valuable tool for decision-making.

Dataset of building footprints in the Yukon Territory. Building footprints were extracted from LiDAR orthophotos using deep learning. Manual corrections were applied to both erroneous and missed building extractions. The Regularize Building Footprint tool was used to reduce vertices and simplify the footprints. A tolerance of 1 was used, with a precision of 0.25.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps). Discover more digital map data and interactive maps from Yukon’s digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

The Versatile Soil Moisture Budget (VSMB) is a soil water budget model that is continuous and deterministic in nature and was developed by AAFC. It is based on the premise that the water available for plant growth is gained by precipitation or irrigation, and lost through evapotranspiration and runoff as well as lateral and deep drainage. The daily net loss or gain is added or subtracted from the water already present in the rooting zone. Water is withdrawn simultaneously, but at different rates, from different soil depths, depending on the potential evapotranspiration, the stage of crop development, the water release characteristics of each soil layer and the available water.

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

The shallow substrate bottom type model was created to support near shore habitat modelling. Data sources include both available observations of bottom type and environmental predictor layers including oceanographic layers, fetch, and bathymetry and its derivatives. Using weighted random forest classification from the ranger R package, the relationship between observed bottom type and predictor layers can be determined, allowing bottom type to be classified across the study areas. The predicted raster files are classified as follows: 1) Rock, 2) Mixed, 3) Sand, 4) MudThe categorical substrate model domains are restricted to the extent of the input bathymetry layers (see data sources) which is 5 km from the 50 m depth contour.

The Coastal Ice Ocean Prediction System (CIOPS) provides a 48 hour ocean and ice forecast over different domains (East, West, Salish Sea) four times a day at 1/36° resolution. A pseudo-analysis component is forced at the ocean boundaries by the Regional Ice Ocean Prediction System (RIOPS) forecasts and spectrally nudged to the RIOPS solution in the deep ocean. Fields from the pseudo-analysis are used to initialize the 00Z forecast, whilst the 06, 12 and 18Z forecasts use a restart files saved at hour 6 from the previous forecast. The atmospheric fluxes for both the pseudo-analysis and forecast components are provided by the High Resolution Deterministic Prediction System (HRDPS) blended both spatially and temporally with either the Global Deterministic Prediction System (GDPS) (for CIOPS-East) or an uncoupled component of the Global Deterministic Prediction System (GDPS) at 10km horizontal resolution (for CIOPS-West) for areas not covered by the HRDPS.

This dataset contains the modelled and observed data used in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). An application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations.

Curie point depth (CPD) mapping in Yukon was done using public domain aeromagnetic data from Natural Resources Canada. In this study, two different CPD methodologies were employed using two different window sizes (200 km and 300 km). Qualitatively, the results were broadly consistent regardless of the method or window size. South-central Yukon exhibits shallow CPD values while northern and southeastern Yukon have deeper CPD values. This suggests that south-central Yukon has higher levels of heat flow in the mid-to-lower crust compared to the rest of the territory. The CPD results are largely consistent with heat flow measurements from the near surface. Specifically, regions with shallow CPD estimates correspond to areas with elevated heat flow measurements. Geologically, the regions with shallow CPD correspond to the Cordillera, while deep CPD areas appear to be co-located with continental platform rocks of Ancestral North America. Comparison with Yukon-specific crustal geotherms derived from other data suggest that the CPD estimates for south-central Yukon are systematically too deep by 2 to 12 km. The discrepancy is likely caused by the need to better understand and account for the fractal distribution of magnetization in the crust in Yukon. The results of this CPD study are valuable in that 95% of Yukon has been demarcated into regions of shallow CPD (higher heat flow) and deep CPD (lower heat flow). These findings should be combined with other data, such as heat generation and sediment thickness estimates, to identify the most prospective regions of elevated subsurface heat in Yukon. Contours have been created for the gridded curie point depth at 1 km intervals and are presented along with the grid.Distributed from [GeoYukon](https://mapservices.gov.yk.ca/GeoYukon/) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)