The National Interest Land Mass (NILM) comprises a set of lands (mainly-NCC owned) considered essential to the functioning and experience of Canada's Capital. The NILM is the dominant tool guiding federal land management and ownership in support of Capital functions. The NILM was approved by Treasury Board in 1998 and the first update was approved in 2001. The 2020 NILM Update is the second update.

TOLLPOINT _OFFICIAL is one of the important layers for Saskatchewan Upgraded Road Network (SURN) and National Road Network (NRN). The TOLLPOINT _OFFICIAL provides the information of road Toll Points to clients that require accurate, relatively up-to-date and detailed description of Saskatchewan Road Network.TOLLPOINT _OFFICIAL, This layer contains data about a place where a right-of-way is charged to gain access to a motorway, a bridge, etc. TOLLPOINT _OFFICIAL contains all the Toll Point locations in the Saskatchewan. TOLLPOINT _OFFICIAL is an important part of the Saskatchewan road network dataset. Each point geometry " TOLLPOINT" has unique Identifiers (NID). "NID" National Identifier is used to manage the updates between data producer and data users.

The Canada Centre for Mapping and Earth Observation (CCMEO) has created a 30m resolution radar mosaic of Canada's landmass from the RADARSAT Constellation Mission (RCM). This product highlights different types of radar interaction with the surface, which can assist the interpretation and study of land cover on a national scale. The national mosaic is made up of 3222 RCM images acquired between August 2023 and February 2024. (Credit: RADARSAT Constellation Mission imagery © Government of Canada [2024]. RADARSAT is an official mark of the CSA.)

The Canadian National Wetlands Inventory (CNWI) is a comprehensive, publicly available national geodatabase developed by the Canadian Wildlife Service (CWS) of Environment and Climate Change Canada (ECCC), in collaboration with federal, provincial, and territorial governments, academia, Indigenous groups, and Non-Governmental Organizations (NGOs). It consists of the best available wetland mapping data, along with its metadata, published in a standardized manner.The CNWI is continuously updated through the compilation of existing data and the acquisition of new high-resolution datasets to address coverage gaps, with an emphasis on peatlands and coastal wetlands, which are key habitats for greenhouse gas (GHG) sequestration. ECCC plans to use the CNWI to train and validate machine-learning algorithms to delineate and classify wetlands at a national scale and to measure trends over time. This will directly support Canada’s Nature-Based Climate Solutions by informing biodiversity conservation, guiding climate change mitigation and adaptation strategies, and supporting GHG emissions reporting.The CNWI was initially released in February 2024 with 13 source datasets. In June 2025, the Inventory was updated to include 14 additional datasets. Collectively, these 27 source datasets comprise approximately 12.1 million wetland polygon features, covering a total area of roughly 640,000 square kilometers across ten provinces and territories (BC, MB, NB, NL, NS, PE, ON, QC, SK, YT). These source datasets were cross-walked into a standardized CNWI classification schema, which is based on two foundational documents: the Canadian Wetland Classification System (National Wetlands Working Group, 1997) and the Canadian Wetland Inventory Data Model (2016).The CNWI Schema contains five major wetland classes (Bog, Fen, Swamp, Marsh, and Shallow/Open Water) and eight subclasses (Rich Fen, Poor Fen, Organic Swamp, Mineral Swamp, Organic Marsh, Mineral Marsh, Shallow Water, and Open Water). Non-conforming wetlands can be categorized into three groups: Peatland, Mixed, and Unclassified. For more information on the CNWI and the related database, please refer to the CNWI User Manual and other supporting documents that accompany this publication. The User Manual provides detailed information on how data are collected, managed, and distributed to meet CNWI data standards.

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Transport Features is composed of, among others, the National Road Network (NRN) and the National Railway Network (NRWN). Transport Features entities are: Nautical Facility, Track Segment, Track Junction, Railway Station, Track Crossing, Track Marker Post, Track Structure, Rail Ferry, Road Segment, Road Ferry, Road Junction, Blocked Passage, Toll Point, Aerial Cableway, Footbridge, Trail, Navigational Aid, Marina, and Runway. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).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@gov.yk. ca](mailto:geomatics.help@yukon.ca)