The LiDAR Point Clouds is a product that is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan.This product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition projects may have been conducted by NRCan or by various partners. The LiDAR point cloud data is licensed under an open government license and has been incorporated into the National Elevation Data Strategy.Point cloud files are distributed by LiDAR acquisition project without integration between projects.The point cloud files are distributed using the compressed .LAZ / Cloud Optimized Point Cloud (COPC) format. The COPC open format is an octree reorganization of the data inside a .LAZ 1.4 file. It allows efficient use and visualization rendering via HTTP calls (e.g. via the web), while offering the capabilities specific to the compressed .LAZ format which is already well established in the industry. Point cloud files are therefore both downloadable for local use and viewable via URL links from a cloud computing environment.The reference system used for all point clouds in the product is NAD83(CSRS), epoch 2010. The projection used is the UTM projection with the corresponding zone. Elevations are orthometric and expressed in reference to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).

Average of the hourly Direct Normal Irradiance (DNI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE").The current version of the National Solar Radiation Database (NSRDB) (v2.0.1) was developed using the Physical Solar Model (PSM), and offers users the solar resource datasets from 1998 to 2014). The NSRDB comprises 30-minute solar and meteorological data for approximately 2 million 0.038-degree latitude by 0.038-degree longitude surface pixels (nominally 4 km2). The area covered is bordered by longitudes 25° W on the east and 175° W on the west, and by latitudes -20° S on the south and 60° N on the north. The solar radiation values represent the resource available to solar energy systems. The AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, a climatological albedo database and mixing ratio, temperature and pressure profiles from Modern Era-Retrospective Analysis (MERRA) to generate cloud masking and cloud properties. Cloud properties generated using PATMOS-x are used in fast radiative transfer models along with aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary sources to estimate Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI). A daily AOD is retrieved by combining information from the MODIS and MISR satellites and ground-based AERONET stations. Water vapor and other inputs are obtained from MERRA. For clear sky scenes the direct normal irradiance (DNI) and GHI are computed using the REST2 radiative transfer model. For cloud scenes identified by the cloud mask, Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data in this layer is an average of the hourly GHI over 17 years (1998-2014). NOTE: The Geographical Information System (GIS) data and maps for solar resources for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) were developed by the U.S. National Renewable Energy Laboratory (NREL) and provided for Canada as an estimate. At present, neither the NREL data, nor the Physical Solar Model (PSM) on which the NREL data is based, have been either assessed or validated for the particular Canadian weather applications. A Canadian GHI map developed by the department of Natural Resources Canada (NRCan) is based on the State University of New York (SUNY) model and has been assessed and validated for the particular Canadian weather applications. The Canadian GHI map is available at http://atlas.gc.ca/cerp-rpep/en/.

The third generation of high resolution 10-m wetland inventory map of Canada, covering an approximate area of one billion hectares, was generated using multi-year (2016-2020), multi-source imagery (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM) Earth Observation (EO) data as well as environmental features. Over 8800 wetland polygons were processed within an object-based random forest classification scheme on the Google Earth Engine cloud computing platform. The average overall accuracy of 90.5% is an increase of 4.7% over CWIM2.CWIM Versions:The Canadian Wetland Inventory Map (CWIM) is an extension of work started at Memorial University to produce a Newfoundland and Labrador wetland inventory during 2015-2018 which was significantly funded by Environment and Climate Change Canada. The first national CWIM was produced 2018-2019 as a collaboration between Memorial University, C-CORE, and Natural Resources Canada. Dr. Brian Brisco was instrumental in connecting ground truth from multiple sources to the project and providing guidance. Version 2 was produced in 2020 which included more training data and processing by Canada’s ecozones rather than provinces to take advantage of the commonality of landscape ecological features within ecozones to improve the accuracy. Version 3 produced in 2021 continued adding more data sources to further improve accuracy specifically an overestimation of wetland area as well as introducing a confidence map. Version 3A completed in 2022 updates only the arctic ecozones due to their relatively lower accuracy and added hydro-physiographic data layers. Currently work is underway to create a northern circumpolar wetland inventory map to be published in 2025.Paper on Newfoundland and Labrador Wetland Inventory:Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Homayouni, S.; Gill, E. The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Remote Sens. 2019, 11, 43. https://doi.org/10.3390/rs11010043Paper on CWIM1:Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Brisco, B., Homayouni, S., Gill, E., … Bourgeau-Chavez, L. (2020). Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Canadian Journal of Remote Sensing, 46(1), 15–33. https://doi.org/10.1080/07038992.2019.1711366Paper on CWIM2:Mahdianpari, M., Brisco, B., Granger, J. E., Mohammadimanesh, F., Salehi, B., Banks, S., … Weng, Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine. Canadian Journal of Remote Sensing, 46(3), 360–375. https://doi.org/10.1080/07038992.2020.1802584Paper on CWIM3:M. Mahdianpari et al., "The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 8789-8803, 2021, doi: 10.1109/JSTARS.2021.3105645.Paper on Arctic ecoregion enhancement for CWIM3A:Michael Merchant, et al., ”Leveraging google earth engine cloud computing for large-scale arctic wetland mapping,” in International Journal of Applied Earth Observation and Geoinformation, vol. 125, 2023, https://doi.org/10.1016/j.jag.2023.103589.

The GNSS (Global Navigation Satellite System), or satellite positioning system, includes all satellite navigation systems. It allows you to know your location, anywhere in the country.Theoretical GNSS specifications estimate the accuracy of the position obtained from a receiver to be approximately 15 meters in planimetry and 25 meters in altimetry. By combining the data with that of another receiver placed on a known geodesic point, the accuracy of the obtained position can vary from a few centimeters to a few meters, depending on the type of receiver used.In order to increase accuracy, the Government of Quebec records data continuously through a network of 18 GNSS stations. These stations are located on geodetic points that are free of any obstacles and capture data from the GPS and GLONASS constellations. Some of these stations receive signals from the Galileo constellation.This data is available in the standard exchange format*Receiver Independent Exchange Format* (RINEX), version 2.11. This format is recognized by the majority of GNSS data processing software.The data is accessible on the __ [ftp server] (ftp://ftp.mrn.gouv.qc.ca/Public/GPS)__) of the MRNF or using the __ [Interactive Map] (https://geodesie.portailcartographique.gouv.qc.ca/)__) of the geodetic network. It should be noted that only data from the last 366 days is kept.The structure of the directories and files on the __ [ftp server] (ftp://ftp.mrn.gouv.qc.ca/Public/GPS)__) as well as the coordinates of the stations are presented in the document __ [GNSS sensor stations] (https://diffusion.mern.gouv.qc.ca/diffusion/RGQ/Documentation/Geodesie/Stations_GNSS.pdf)__.# #État GNSS stations##You can consult the status of the stations in the document __ [Status of GNSS stations] (https://diffusion.mern.gouv.qc.ca/diffusion/RGQ/Documentation/Geodesie/État_Stations_GNSS.pdf)__. You will be notified if a station is in service, out of service, or if equipment maintenance is planned.# #GNSS in real time by cell phoneThe government also offers GNSS data by cellular telephone, which allows centimetric positioning work to be carried out in real time. Users of georeferenced data can thus, with a single multi-frequency GNSS receiver equipped with a modem by cellular telephone, identify or implement any physical detail with an accuracy of a few centimeters in the NAD 83 reference system (SCRS) (period 1997.0).The signal that contains this data is available to everyone. The range depends on telephone coverage, ionospheric conditions and especially on the instruments used. For more information on using GNSS in real time, see document __ [Guidelines for GNSS RTK/RTN Surveys in Canada] (http://publications.gc.ca/site/fra/9.822883/publication.html)__.# #Détails techniquesThe transmission of GNSS data as well as the station's NAD 83 (SCRS) coordinates (period 1997.0) is transmitted by cellular telephony from an IP address on the Internet. Each station transmits its data in one of the following two formats: CMR+ or RTCM V3.2. The document __ [GNSS capture stations] (https://diffusion.mern.gouv.qc.ca/diffusion/RGQ/Documentation/Geodesie/Stations_GNSS.pdf)__) gives for each city the IP address of the CMR+ or RTCM V3.2 formats as well as the antenna model. It should be noted that the data is not broadcast according to the*Networked Transport of RTCM protocol via Internet Protocol* (NTRIP).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features.This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards.CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis.Related Products (Open Maps Links):**[Constructions and Land Use in Canada - CanVec Series - Manmade Features](https://open.canada.ca/data/en/dataset/fd4369a4-21fe-4070-914a-067474da0fd6)****[Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features](https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b)****[Administrative Boundaries in Canada - CanVec Series - Administrative Features](https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97)****[Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features](https://open.canada.ca/data/en/dataset/92dbea79-f644-4a62-b25e-8eb993ca0264)****[Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features](https://open.canada.ca/data/en/dataset/80aa8ec6-4947-48de-bc9c-7d09d48b4cad)****[Transport Networks in Canada - CanVec Series - Transport Features](https://open.canada.ca/data/en/dataset/2dac78ba-8543-48a6-8f07-faeef56f9895)****[Elevation in Canada - CanVec Series - Elevation Features](https://open.canada.ca/data/en/dataset/64aad38d-f692-4ab6-bf2c-f938586c1249)****[Map Labels - CanVec Series - Toponymic Features](https://open.canada.ca/data/en/dataset/b3fdcd34-4533-415f-8f83-68f17f9d5d68)**

GeoAI are buildings, hydrography, forests, and roads automatically extracted using Deep Learning models applied to a source dataset, typically aerial or satellite images. The primary aim of GeoAI is to increase Canada's availability of high-resolution foundational geospatial data for both spatial and temporal coverage.‌The infrastructure and expertise put in place by NRCan enables a rapid, efficient, and scalable data creation process through the use of leading-edge technology and Artificial Intelligence models. Published datasets for a given source can be revisited at a later date as more accurate models are developed and put into production. For now, only static files are available, but as the series develops, new products and services will be added.Several use cases of GeoAI data for change detection have been created. To view them, please follow the links below:- Iqaluit: https://open.canada.ca/data/en/dataset/ef29513d-a007-408c-beba-65f86e7b5b01- Calgary: https://open.canada.ca/data/en/dataset/bd07b597-5166-4b62-ae9c-245b3c62ea03- Québec City: https://open.canada.ca/data/en/dataset/4026720e-f7fb-478e-a58f-c62ebe20005d- Winnipeg: https://open.canada.ca/data/en/dataset/a6874aac-cfcd-4b92-bb3f-6031eedb5b57- Victoria: https://open.canada.ca/data/en/dataset/f011c7b9-f79e-42f8-9481-9bf9788bb189- Trois-Rivières: https://open.canada.ca/data/en/dataset/2b11a323-52c2-43d5-9c09-bc4b15b1bcc8

This series of historic flood susceptibility maps comes from an XGBboost machine learning model trained on major floods from 2005 to 2023. The trained model is then run for each year from 2000 to 2023, including unique temporal characteristics of temperature, precipitation, land use land cover and Normalized Difference Vegetation Index (NDVI), to predict the flood susceptibility of any given year.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)- [Future - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)

Compiled by Alberta Data Partnerships Ltd. (ADP), on behalf of the Government of Alberta, the ATS v4.1 Polygons - Section with Road Allowance layer contains polygons that represent the location of Sections and adjacent Road Allowance Segment polygons, derived from the Master Alberta Township System points file published as ATS Version 4.1, dated March 31, 2005, and clipped to an updated Alberta Data Partnerships Ltd. (ADP) created version of the Alberta provincial boundary.

Compiled by Alberta Data Partnerships Ltd. (ADP), on behalf of the Government of Alberta, the ATS v4.1 Polygons - Quarter Section with Road Allowance layer contains polygons that represent the location of Quarter Sections and adjacent Road Allowance Segment polygons, derived from the Master Alberta Township System points file published as ATS Version 4.1, dated March 31, 2005, and clipped to an updated Alberta Data Partnerships Ltd. (ADP) created version of the Alberta provincial boundary.

Compiled by Alberta Data Partnerships Ltd. (ADP), on behalf of the Government of Alberta, the ATS v4.1 Polygons - Quarter Section with Road Allowance layer contains polygons that represent the location of Quarter Sections and adjacent dissolved Road Allowance Segment polygons, derived from the Master Alberta Township System points file published as ATS Version 4.1, dated March 31, 2005, and clipped to an updated Alberta Data Partnerships Ltd. (ADP) created version of the Alberta provincial boundary.