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Print/export Download as PDF; Printable version; In other projects ... id is the document ID in FDOT's url scheming. For example, the ID for this URL is 104338.
The Florida Department of Transportation (FDOT) is a decentralized agency charged with the establishment, maintenance, and regulation of public transportation in the state of Florida. [1] The department was formed in 1969. It absorbed the powers of the State Road Department (SRD). The current Secretary of Transportation is Jared W. Perdue.
This is a list of GIS data sources (including some geoportals) that provide information sets that can be used in geographic information systems (GIS) and spatial databases for purposes of geospatial analysis and cartographic mapping. This list categorizes the sources of interest.
The functional classification of a road is the class or group of roads to which the road belongs. There are three main functional classes as defined by the United States Federal Highway Administration : arterial, collector, and local.
Geospatial metadata (also geographic metadata) is a type of metadata applicable to geographic data and information.Such objects may be stored in a geographic information system (GIS) or may simply be documents, data-sets, images or other objects, services, or related items that exist in some other native environment but whose features may be appropriate to describe in a (geographic) metadata ...
The AASHTO Soil Classification System was developed by the American Association of State Highway and Transportation Officials, and is used as a guide for the classification of soils and soil-aggregate mixtures for highway construction purposes.
The Vector Map (VMAP), also called Vector Smart Map, is a vector-based collection of geographic information system (GIS) data about Earth at various levels of detail. Level 0 (low resolution) coverage is global and entirely in the public domain. Level 1 (global coverage at medium resolution) is only partly in the public domain.
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.