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Not all National Topographic System maps strictly follow the National Tiling System's linear grid. Some maps also, as an "overedge", cover land in an area which would otherwise be covered by an adjacent map sheet, simply because the latter area does not contain enough land in Canada to warrant a separate printing. [4] [clarification needed]
GeoBase data elements carry a unique and singular ID. GeoBase data comply with a standard data model and meet or exceed a minimum standard for accuracy, resolution, and currency. Metadata is defined and standardized at the entity level. GeoBase data and all associated tools adhere to international standards. GeoBase data is seamless across Canada.
The Canada Geographic Information System (CGIS) was an early geographic information system (GIS) developed for the Government of Canada beginning in the early 1960s. CGIS was used to store geospatial data for the Canada Land Inventory and assisted in the development of regulatory procedures for land-use management and resource monitoring in Canada.
The USGS Gap Analysis Program maintains four primary data sets: land cover, protected areas, species and aquatic. The GAP Land Cover Data Set is the most complete map ever produced of vegetative associations for the US. Classified into 551 ecological systems, and 32 modified ecological systems (where human impacts have had an effect).
Open data in Canada describes the capacity for the Canadian Federal Government and other levels of government in Canada to provide online access to data collected and created by governments in a standards-compliant Web 2.0 way. Open data requires that machine-readable should be made openly available, simple to access, and convenient to reuse. [1]
A national lidar dataset refers to a high-resolution lidar dataset comprising most—and ideally all—of a nation's terrain. Datasets of this type typically meet specified quality standards and are publicly available for free (or at nominal cost) in one or more uniform formats from government or academic sources.
Aerial photography, land cover data, and digital elevation models all provide coverage data. Generally, a coverage can be multi-dimensional, such as 1-D sensor timeseries, 2-D satellite images, 3-D x/y/t image time series or x/y/z geo tomograms , or 4-D x/y/z/t climate and ocean data.
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.