The 2010 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Programs that perform a laboratory analysis of body fluids and tissue to determine the presence of irregularities or to identify unknown substances submitted for analysis. Definition is protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)

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

The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of the amount of precipitation that occurred over recent past periods of 6 or 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar, satellite imagery and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at horizontal resolution of 10 km. Data is only available for the surface level. Analysis data is made available four times a day for 6h intervals and twice a day for the 24h interval. A preliminary estimate is available approximately 1h after the end of the accumulation period, and revised 7h after in order to assimilate gauge data arriving later.

Temporal analysis of changes in Calgary, Alberta, based on GeoAI features automatically extracted from satellite images acquired in 2011 and 2021. 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 that were not detected in 2011. The addition of new buildings is a good indicator of urban development and/or sprawl. Using the same approach, GeoAI multidate roads enable the detection of Statistics Canada's National Roads Network (NRN) segments present in 2021 and/or in 2011. The development of new roads is also 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

This data set contains the polygons delimiting the woods of the agglomeration of Montreal.The boundaries of the woods and their composition are the result of an analysis of aerial photos and/or of field visits and/or of particular ecological studies. These are constantly updated in accordance with the advancement of knowledge in the community. Some plant communities and their limits may therefore be imprecise.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Temporal analysis of changes for Victoria, British Columbia, based on GeoAI features automatically extracted from satellite images acquired in the 2000s, 2010s and 2020s. 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 the 2000s, 2010s and 2020s. The addition of new buildings is a good indicator of urban development and/or sprawl. Using the same approach, GeoAI multidate roads enable the detection of Statistics Canada's National Roads Network (NRN) segments present in the 2000s, 2010s and 2020s. The development of new roads is also indicator of urban development and/or sprawl. Complementarily, an analysis of changes in the forest coverage from the GeoAI datasets is done between the 2000s and the 2020s. 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

The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of precipitation amounts that occurred over a period of 24 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar, satellite imagery and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at a horizontal resolution of 10 km. The 24 hour analysis is produced twice a day and is valid at 06 and 12 UTC. A preliminary analysis is available approximately 1 hour after the end of the accumulation period and a final one is generated 7 hours later in order to assimilate more gauge data.

Temporal analysis of changes in Quebec city based on GeoAI features automatically extracted from satellite images acquired in 2006 and 2022. Simple geospatial analysis enables the detection of features present in 2022 that were not already there in 2006. 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

The Regional Deterministic Precipitation Analysis (RDPA) produces a best estimate of precipitation amounts that occurred over a period of 6 hours. The estimate integrates data from in situ precipitation gauge measurements, weather radar, satellite imagery and numerical weather prediction models. Geographic coverage is North America (Canada, United States and Mexico). Data is available at a horizontal resolution of 10 km. The 6 hour analysis is produced 4 times a day and is valid at 00, 06, 12 and 18 UTC. A preliminary analysis is available approximately 1 hour after the end of the accumulation period and a final one is generated 7 hours later in order to assimilate more gauge data.