“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. The feature classes of this product delineate polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.The first feature class, Automatically Extracted Buildings by acquisition source, contains building footprints delivered according to the spatial extent of each source dataset used for extraction. When the spatial extents of acquisition sources overlap, footprints for the same building may therefore be duplicated in this class.The second feature class, Optimized Buildings Layer, is an assembled and harmonized layer derived from the buildings by acquisition source. Its objective is to provide a unique representation of each building footprint by removing duplicates and resolving overlaps between sources.

Major buildings and municipal services.attributes:ID - unique identifierSubtype - Item subtypeName - Building or department name**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Physical Exposure component of the National Human Settlement Layer (NHSL), defined here as the ‘Physical Exposure Model’, includes a delineation of settled areas and related land use across Canada, as well as information about buildings, persons, and building replacement values (structure and contents) within those areas.Buildings within the inventory are classified using a combination of occupancy types, engineering-based construction types adopted for Canada, and design levels representing the approximate building code requirements at the time of construction. The inventory is derived from detailed housing statistics provided at the dissemination area level as part of the 2016 national census and from georeferenced business listings. Building populations at different times of day are estimated for standard daytime hours (9am-5pm); for morning and evening commute hours (7am-9am; 5pm-7pm), and; for nighttime hours when the majority of people are home (7pm-7am). Replacement values are provided for structural, nonstructural, and contents components of buildings, based on industry replacement costs for representative regions across Canada.The physical exposure model is provided in two formats: (1) According to settled areas (i.e., polygons), which are areas that approximately delineate clusters of buildings across Canada. Summary statistics about buildings and populations within each settled area boundary are provided. (2) According to building archetypes (i.e., points) within settled areas. These are represented as point locations at the centroid of the corresponding settled area, and each settled area can have multiple point features corresponding to different building archetypes present within that area. In total, the model characterizes 35.2 million people in 9.7 million buildings across 390,000 locations with a total approximate replacement value of $8.2 trillion (2019 CAD) including contents.

To outline the locations of buildings on Parks Canada sites, buildings that Parks Canada manages, and other buildings of interest to Parks Canada. Polygon file to map building footprints of buildings on Parks Canada sites. Footprints may be derived by tracing the roof outline (for example from an airphoto) or using more detailed measurements of the ground floor.Data is not necessarily complete - updates will occur weekly.

Temporal analysis of changes in Trois-Rivières, Québec, based on GeoAI features automatically extracted from satellite images acquired in 2013 and 2021-22. Simple geospatial analysis intersecting Statistics Canada's Open Database of Buildings, version 3 (ODB v3) with GeoAI multidate building features enables the detection of buildings observed in 2021-22 that were not detected in 2013. The addition of new buildings is a good indicator of urban development and/or sprawl.‌GeoAI enables temporal coverage of various areas in Canada, thus providing a useful tool for change detection and trend analysis at high resolution. While the series is still fairly new, and such examples are limited for the time being, NRCan strives to gradually increase its GeoAI data offering for both spatial and temporal coverage.For more information about the GeoAI - GeoBase Series, please visit the following link: https://open.canada.ca/data/en/dataset/74738ff5-5367-5958-9aee-98fffdcd1876

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)

Inventory of building footprints in the City of Rouyn-Noranda.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

644 datasets of Typical Meteorological Years (TMY) created by joining twelve Typical Meteorological Months selected from a database of up to 20 years of CWEEDS hourly data. The months are chosen by statistically comparing individual monthly means with long-term monthly means for daily total global solar irradiance, mean, minimum and maximum dry bulb temperature, mean, minimum and maximum dew point temperature, and mean and maximum wind speed. These hourly datasets are used by the engineering and scientific community mainly as inputs for solar system design and analysis and building energy systems analysis tools. This dataset has been updated with the most recent changes made in March 2023. The solar values in these files are based on 0.1° x 0.1° (11 km x 11 km grid) for all of Canada. Refer to Data Resources below for additional information on the TMY file format.

Temporal analysis of changes in the Iqaluit region, Nunavut, based on GeoAI features automatically extracted from satellite images acquired in 2012 and 2022. Simple geospatial analysis intersecting GeoAI multidate building features enables the detection of buildings observed in 2022 that were not detected in 2012. The addition of new buildings is a good indicator of urban development and/or sprawl.GeoAI enables temporal coverage of various areas in Canada, thus providing a useful tool for change detection and trend analysis at high resolution. While the series is still fairly new, and such examples are limited for the time being, NRCan strives to gradually increase its GeoAI data offering for both spatial and temporal coverage.For more information about the GeoAI - GeoBase Series, please visit the following link: https://open.canada.ca/data/en/dataset/74738ff5-5367-5958-9aee-98fffdcd1876

Temporal analysis of changes in Winnipeg, Manitoba, based on GeoAI features automatically extracted from satellite images acquired in 2013 and 2023. Simple geospatial analysis enables the detection of features present in 2023 that were not already there in 2013. The addition of new buildings is a good indicator of urban development and/or sprawl. Complementarily, an analysis of changes in the forest coverage from the GeoAI datasets is done. This analysis reflects the gains and losses between both dates.‌GeoAI enables temporal coverage of various areas in Canada, thus providing a useful tool for change detection and trend analysis at high resolution. While the series is still fairly new, and such examples are limited for the time being, NRCan strives to gradually increase its GeoAI data offering for both spatial and temporal coverage.For more information about the GeoAI - GeoBase Series, please visit the following link: https://open.canada.ca/data/en/dataset/74738ff5-5367-5958-9aee-98fffdcd1876