Geodaten und Metadaten/en

<languages/> Geodata and Metadata

"Geospatial data is defined in Article 3 (2) of Directive 2007/2 / EC (Inspire Directive) as" data directly or indirectly related to a specific location or geographical area. "Spatial data thus describes an object, either directly ( by coordinates) or indirectly (eg by zip code), a landscape or its position in space, spatial data can be linked together to create detailed queries and analyzes.

A further division of the geodata takes place in geobasis data and Geofachdaten:

1. Geographic reference data 'means landscape and property descriptive data, mainly from cadastral and surveying. Specifically, the geospatial reference data set comprises the existing data from ALK, ALB and ATKIS as well as the previously separately managed DTM and the scanned topographical map works. It also includes data on reference systems and core networks, as well as administrative boundaries at national, regional and local (e.g., parcel) level. In the future, this will include image data such as orthophotos, aerial and satellite imagery. They are necessary basic information for the spatial mapping of Geofachdaten.

2. 'Geofachdaten' are the data collected in certain disciplines with spatial reference. Geofachdaten be u.a. on the basis of technical regulations (for example for statistics, soil, nature conservation etc.) in the administrations of the federal states and the federation. They can be presented as an overlay on geospatial reference data.

In addition to geodata, metadata is an integral part of a geodata infrastructure. They are indispensable when searching, organizing, managing and archiving geodata. Metadata, also known as meta-information, is "data about data". They provide the user with a structured description of the actual geodata and geoservices. The geodata is described in terms of content including the factual data and attributes, format, extent, quality, spatial reference and distribution. With their information content, metadata allows avoiding redundant data collection, uncovering existing gaps in the data sets, standardizing data and terms, quality assurance for the data sets, comparisons between alternative data sets, and generating transparency of the geospatial data market.