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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).
Topographic mapping in Canada was originally undertaken by many different agencies, with the Canadian Army’s Intelligence Branch forming a survey division to create a more standardized mapping system in 1904. The indexing system used today was established in 1923, and the map catalogue officially became the National Topographic System in 1926 ...
The Land Title and Survey Authority of British Columbia (LTSA) is a publicly accountable, statutory corporation which operates and administers the land title and survey systems in British Columbia, Canada. The LTSA delivers secure land titles through timely, efficient registration of land title interests and survey records; these services are ...
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.
The NMIG (National Mapping Information Group) of Geoscience Australia is the Australian Government's national mapping agency. It provides topographic maps and data to meet the needs of the sustainable development of the nation. The Office of Spatial Data Management provides an online free map service MapConnect. [12]
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.
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.
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.