Coverage data

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A coverage is the digital representation of some spatio-temporal phenomenon. ISO 19123 provides the definition:

Contents

Coverages play an important role in geographic information systems (GIS), geospatial content and services, GIS data processing, and data sharing.

A coverage is represented by its "domain" (the universe of extent) and a collection representing the coverage's values at each defined location within its range. For example, a satellite image derived from remote sensing might record varying degrees of light pollution. 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.

However, coverages are more general than just regularly gridded imagery. The corresponding standards (see below) address regular and irregular grids, point clouds, and general meshes.

An interoperable service definition for navigating, accessing, processing, and aggregation of coverages is provided by the Open Geospatial Consortium (OGC) Web Coverage Service (WCS) suite and Web Coverage Processing Service (WCPS), a spatio-temporal coverage query language.

Standards

Coverages represent digital geospatial information representing space/time-varying phenomena. OGC Abstract Topic 6 [1] - which is identical to ISO 19123 - defines an abstract model of coverages. Many implementations are conceivable which all conform to this abstract model while not being interoperable. This abstract coverage model is concretized to the level of interoperability by the OGC standard GML 3.2.1 Application Schema - Coverages [2] (often referred to as GMLCOV) which in turn is based on the Geography Markup Language (GML) 3.2, [3] an XML grammar written in XML Schema for the description of application schemas as well as the transport and storage of geographic information.

The European legal framework for a unified Spatial Data Infrastructure, INSPIRE, in its Annex II and III relies on the OGC definitions of coverages as well, but modifies them in places in a way making them less compatible and interoperable with the OGC standard. For example, components of the coverage concept are selectively recombined into new, different definitions of a coverage.

Coverage model

Formally, in GMLCOV AbstractCoverage is a subtype of AbstractFeature (indicating its close relation). An abstract coverage consists of the following components:

This abstract coverage is refined into several concrete coverage types, which can be instantiated, for example:

Among the special cases which can be modeled by coverages are

Relationship to Features

A coverage is a special kind of geographic feature, with the distinguishing characteristics that other features have one particular value associated (such as a road number, which remains constant over all the road's extent) whereas a coverage typically conveys different values at different locations within its domain. ISO 19109 (2nd Ed.) explains the relationship between features and coverages as follows (clause 7.2.2):

Both viewpoints are required since they each express a fundamental meta-model of the world: as a space populated by things, or as a space within which properties vary. Furthermore, requirements relating to both viewpoints may occur in a single application, typically matching a data-flow: from observation through interpretation, and then elaboration and simulation. [4]

Coverage encoding

Different coverage encodings Coverage encoding variants.png
Different coverage encodings

The format-independent logical structure of coverages can be mapped to GML (such as for sensor time series) or to any of a series of data formats, such as GeoTIFF, NetCDF, HDF-EOS, or NITF.

As some of these encoding formats are not capable of incorporating all metadata making up a coverage, the coverage model foresees a multipart MIME encoding (see Figure) where the first component encodes the coverage description (domain extent, range type, metadata, etc.) and the second part consists of the range set "payload" using some encoding format.

Services

In Web services following the open OGC standards, coverages can be used by various service types:

Industry Terminology: GIS format

Early GIS systems were often characterised as either 'raster' or 'vector' systems, depending on the underlying approach to handling geometry. Raster GIS could be interpreted as using a regular discrete coverage model, while Vector GIS are more feature-oriented. The term "coverage" was most notably applied to the legacy ARC/INFO (ArcInfo) format developed by ESRI. At that time this was a novel concept, extending CAD formats into more spatially aware data that featured linked attributes. This usage was consistent with the coverage concept discussed here, in the sense that an ArcInfo coverage provided a one-to-one mapping from space to the thematic value or classification for each layer or coverage. However, ArcInfo coverages had a particular topological approach to ensure completeness and uniqueness, processed using the BUILD and CLEAN commands are 2D planar datasets that maintain topological information, thus a polygon "knows" which segments of its perimeter it shares with adjacent polygons. Due to the lack of processing power in computing at the time of its development[ when? ], the Coverage model employs indexed binary files to store spatial and attribute data separately as opposed to utilizing a RDBMS. [5]

This has changed with the advent of raster database technology like rasdaman which makes efficient ad hoc filtering and processing feasible. [6] [7]

Related Research Articles

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<span class="mw-page-title-main">Geographic information system</span> System to capture, manage and present geographic data

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<span class="mw-page-title-main">Geography Markup Language</span> XML grammar for geographical features

The Geography Markup Language (GML) is the XML grammar defined by the Open Geospatial Consortium (OGC) to express geographical features. GML serves as a modeling language for geographic systems as well as an open interchange format for geographic transactions on the Internet. Key to GML's utility is its ability to integrate all forms of geographic information, including not only conventional "vector" or discrete objects, but coverages and sensor data.

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<span class="mw-page-title-main">CityGML</span>

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<span class="mw-page-title-main">3D city model</span>

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A geographic data model, geospatial data model, or simply data model in the context of geographic information systems, is a mathematical and digital structure for representing phenomena over the Earth. Generally, such data models represent various aspects of these phenomena by means of geographic data, including spatial locations, attributes, change over time, and identity. For example, the vector data model represents geography as collections of points, lines, and polygons, and the raster data model represent geography as cell matrices that store numeric values. Data models are implemented throughout the GIS ecosystem, including the software tools for data management and spatial analysis, data stored in a variety of GIS file formats, specifications and standards, and specific designs for GIS installations.

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<span class="mw-page-title-main">Open Geospatial Consortium</span> Standards organization

The Open Geospatial Consortium (OGC), an international voluntary consensus standards organization for geospatial content and location-based services, sensor web and Internet of Things, GIS data processing and data sharing. It originated in 1994 and involves more than 500 commercial, governmental, nonprofit and research organizations in a consensus process encouraging development and implementation of open standards.

The Sensor Observation Service (SOS) is a web service to query real-time sensor data and sensor data time series and is part of the Sensor Web. The offered sensor data consists of data directly from the sensors, which are encoded in the Sensor Model Language (SensorML), and the measured values in the Observations and Measurements encoding format. The web service as well as both file formats are open standards and specifications of the same name defined by the Open Geospatial Consortium (OGC).

<span class="mw-page-title-main">Array DBMS</span> System that provides database services specifically for arrays

Array database management systems provide database services specifically for arrays, that is: homogeneous collections of data items, sitting on a regular grid of one, two, or more dimensions. Often arrays are used to represent sensor, simulation, image, or statistics data. Such arrays tend to be Big Data, with single objects frequently ranging into Terabyte and soon Petabyte sizes; for example, today's earth and space observation archives typically grow by Terabytes a day. Array databases aim at offering flexible, scalable storage and retrieval on this information category.

The Web Coverage Processing Service (WCPS) defines a language for filtering and processing of multi-dimensional raster coverages, such as sensor, simulation, image, and statistics data. The Web Coverage Processing Service is maintained by the Open Geospatial Consortium (OGC). This raster query language allows clients to obtain original coverage data, or derived information, in a platform-neutral manner over the Web.

A GeoPackage (GPKG) is an open, non-proprietary, platform-independent and standards-based data format for geographic information system implemented as a SQLite database container. Defined by the Open Geospatial Consortium (OGC) with the backing of the US military and published in 2014, GeoPackage has seen widespread support from various government, commercial, and open source organizations.

References

  1. Topic 6 - Schema for coverage geometry and functions, OGC 07-011
  2. OGC GML Application Schema - Coverages, OGC 09-146r2
  3. OpenGIS Geography Markup Language (GML) Encoding Standard, OGC 07-036
  4. A Woolf; S J D Cox; C Portele (2010). "Data Harmonization - GEOSS AIP-3 Contribution" (PDF). doi:10.13140/RG.2.1.1840.4569. Archived from the original (PDF) on 2015-10-17. Retrieved 2016-01-27.
  5. Zeiler, Michael. Modeling Our World, The ESRI Guide to Geodatabase Design. ESRI Press, 1999. ISBN   1-879102-62-5
  6. Baumann, P.; Jucovschi, C.; Stancu-Mara, S.: Efficient Map Portrayal Using a General-Purpose Query Language (A Case Study). DEXA 2009, August 31 - September 04, 2009, Vienna, Austria, Springer Berlin/Heidelberg, LNCS 5690, pp. 153-163
  7. Jucovschi, C., Baumann, P., Stancu-Mara, S.: Speeding up Array Query Processing by Just-In-Time Compilation. IEEE Intl Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-08), Pisa, Italy, 15 December 2008, pp. 408 - 413