Glossary of
\n\tterms commonly used in geospatial technology <\/p>\n
Here is a glossary of terms commonly used in geospatial
\ntechnology: <\/p>\n
AIS: Automatic Identification System (AIS) is a tracking
\n\tsystem used in maritime applications. It uses VHF radio signals to
\n\tcollect and transmit vessel information such as position, speed, and
\n\tidentification. <\/p>\n
Analytic ready Data (ARD): Analytic Ready Data (ARD) refers
\n\tto pre-processed geospatial data that has been standardized,
\n\tcalibrated, and enhanced to facilitate analysis and modelling tasks. <\/p>\n
AOI:
\n AOI stands for Area of Interest. It represents a
\n\tspecific geographic region or location that is the focus of analysis
\n\tor investigation. <\/p>\n
Attribute Data: It refers to non-spatial information
\n\tassociated with spatial data. For example, in a GIS, attribute data
\n\tcould include population figures, land use categories, or
\n\ttemperature values. <\/p>\n
Cartography
\n : It is the art and science of mapmaking. It
\n\tinvolves the creation, study, and interpretation of maps, including
\n\ttheir design, production, and use. <\/p>\n
CE90:
\n CE90 stands for Circular Error 90%. It is a measure of
\n\tpositional accuracy in remote sensing and describes the radial
\n\tdistance from the true location within which 90% of points fall. <\/p>\n
DEM:
\n DEM stands for Digital Elevation Model. It is a digital
\n\trepresentation of the Earth’s surface that displays the elevation
\n\tvalues of terrain features. <\/p>\n
Digital Elevation Model (DEM):
\n It is a digital representation
\n\tof the Earth’s surface topography. A DEM provides elevation values
\n\tfor each point on the surface and is often used in terrain analysis
\n\tand 3D visualization. <\/p>\n
Digital Surface Model (DSM):
\n It is a digital representation
\n\tof the Earth’s surface, including both natural and man-made
\n\tfeatures. DSMs capture the elevation of objects on the Earth’s
\n\tsurface, such as buildings, vegetation, and terrain. <\/p>\n
Digital Terrain Model (DTM):
\n It is a digital representation
\n\tof the bare Earth’s surface, excluding above-ground features such as
\n\tbuildings and vegetation. DTM focuses on the topography and relief
\n\tof the terrain, providing elevation data without any additional
\n\tstructures. <\/p>\n
Earth observation (EO) satellite
\n : EO Satellite also known as
\n\ta remote sensing satellite, is a type of satellite designed to
\n\tcapture data and images of the Earth’s surface from space. These
\n\tsatellites are equipped with various sensors and instruments that
\n\tallow them to gather information about the planet’s atmosphere,
\n\tland, oceans, and other features. <\/p>\n
Earth Observation:
\n It refers to the process of collecting
\n\tinformation about the Earth’s surface using remote sensing
\n\ttechnologies, such as satellites or aircraft. Earth observation
\n\tprovides valuable data on various aspects, including land cover,
\n\tclimate patterns, and environmental changes. <\/p>\n
Electro-optical (EO):
\n Electro-optical (EO) refers to the
\n\ttechnology and systems that combine electronics and optics for
\n\tsensing and imaging applications. <\/p>\n
FTP:
\n FTP stands for File Transfer Protocol. It is a standard
\n\tnetwork protocol used for transferring files between computers over
\n\ta network. It is commonly used to upload and download geospatial
\n\tdata. <\/p>\n
Geoanalytics:
\n It is the process of analyzing geospatial data
\n\tto extract meaningful insights and make informed decisions.
\n\tGeoanalytics involves techniques such as spatial statistics, data
\n\tmining, and modelling to understand spatial relationships and
\n\tpatterns. <\/p>\n
Geocaching
\n : Geocaching is an outdoor recreational activity
\n\twhere participants use GPS or mobile devices to navigate to hidden
\n\tcontainers (called geocaches) located at specific coordinates. It
\n\tcombines technology, exploration, and treasure hunting. <\/p>\n
Geocoding:
\n Geocoding is the process of assigning geographic
\n\tcoordinates (latitude and longitude) to addresses or other
\n\tlocation-based data. It enables the mapping and analysis of
\n\taddress-based information. <\/p>\n
Geodatabase
\n : A Geodatabase is a database specifically
\n\tdesigned to store, manage, and analyze geospatial data. It provides
\n\ta structured framework for organizing and maintaining spatial
\n\tinformation. <\/p>\n
Geofencing:
\n It is a virtual boundary defined in geospatial
\n\ttechnology to trigger a specific action or notification when a
\n\tmobile device or object enters or exits the designated area.
\n\tGeofencing has various applications, such as location-based
\n\tmarketing, asset tracking, and safety monitoring. <\/p>\n
Geographic Information System (GIS): It is a system designed
\n\tto capture, store, analyze, manage, and present spatial or
\n\tgeographic data. It allows for the integration of different types of
\n\tdata, such as maps, satellite imagery, and demographic information. <\/p>\n
Geoinformation:
\n It is the collective term used for all types
\n\tof geospatial data and related information. Geoinformation includes
\n\tmaps, satellite imagery, sensor data, attribute data, and any other
\n\tdata with a spatial component. <\/p>\n
GeoIntelligence:
\n It refers to the integration of geospatial
\n\tdata, analysis, and visualization techniques to support
\n\tdecision-making processes. GeoIntelligence combines spatial
\n\tinformation with other data sources to provide valuable insights for
\n\tplanning, resource management, and risk assessment. <\/p>\n
Geoportal: It is a web-based platform or portal that provides
\n\taccess to geospatial data, services, and applications. Geoportals
\n\toften offer search and discovery tools, data download options, and
\n\tmapping capabilities to enable users to find and utilize geospatial
\n\tinformation easily. <\/p>\n
Georeferencing:
\n Georeferencing is the process of aligning
\n\tspatial data to a specific coordinate system, allowing it to be
\n\taccurately displayed and analyzed within a geospatial framework. <\/p>\n
Geospatial Metadata:
\n It refers to the information that
\n\tdescribes the characteristics, quality, and content of geospatial
\n\tdata. Geospatial metadata includes details such as data source,
\n\taccuracy, projection, and update frequency, which help users
\n\tunderstand and assess the data’s suitability for their needs.<\/p>\n
Geospatial Technology:
\n It refers to the technology that is
\n\tused to acquire, store, manipulate, analyze, and visualize spatial
\n\tdata. <\/p>\n
Geospatial Web Services:
\n They are standardized protocols and
\n\tinterfaces that allow geospatial data and processing capabilities to
\n\tbe shared over the internet. Geospatial web services enable data
\n\tintegration, interoperability, and distributed processing across
\n\tdifferent platforms and applications. <\/p>\n
GIS:
\n GIS stands for Geographic Information System. It is a
\n\tsystem designed to capture, store, analyze, and display geospatial
\n\tdata. GIS combines hardware, software, data, and methods for
\n\tmanaging and analyzing geographic information. <\/p>\n
Global Positioning System (GPS):
\n It is a satellite-based
\n\tnavigation system that provides precise location and time
\n\tinformation anywhere on Earth. GPS is widely used for mapping,
\n\tnavigation, and surveying purposes. <\/p>\n
GNSS:
\n GNSS stands for Global Navigation Satellite System. It
\n\tis a collective term used to refer to navigation systems that use
\n\tmultiple satellite constellations, including GPS, GLONASS, Galileo,
\n\tand BeiDou. <\/p>\n
GPS: GPS stands for Global Positioning System. It is a
\n\tsatellite-based navigation system that allows users to determine
\n\ttheir precise location on the Earth’s surface. <\/p>\n
GSD:
\n GSD stands for Ground Sampling Distance. It represents
\n\tthe distance on the ground that each pixel in an image or dataset
\n\trepresents. Smaller GSD values indicate higher spatial resolution. <\/p>\n
Incidence Angle:
\n Incidence Angle refers to the angle at which
\n\tan electromagnetic wave strikes the Earth’s surface. In remote
\n\tsensing, it affects the quality and interpretation of satellite
\n\timagery. <\/p>\n
Land Use\/Land Cover
\n : Land Use\/Land Cover refers to the
\n\tcategorization and classification of the Earth’s surface based on
\n\tthe activities or materials found on it (land use) and the physical
\n\tor biological characteristics of the surface (land cover). <\/p>\n
LiDAR (Light Detection and Ranging): It is a remote sensing
\n\ttechnology that uses laser pulses to measure distances to the
\n\tEarth’s surface. LiDAR data is commonly used for creating highly
\n\taccurate terrain models and 3D point clouds. <\/p>\n
MWIR:
\n MWIR stands for Mid-Wave Infrared. It refers to the
\n\tportion of the electromagnetic spectrum that lies between the SWIR
\n\tand long-wave infrared (LWIR) ranges. MWIR imagery is used for
\n\tthermal imaging and certain material analyses. <\/p>\n
NIR:
\n NIR stands for Near-Infrared. It refers to the portion
\n\tof the electromagnetic spectrum that lies adjacent to the visible
\n\tlight spectrum. NIR imagery is useful for vegetation analysis and
\n\tother applications. <\/p>\n
ONA:
\n ONA stands for Off-Nadir Angle. It refers to the angle
\n\tbetween the line of sight from a satellite sensor to the ground and
\n\tthe vertical direction directly beneath the satellite. <\/p>\n
Optical Imagery:
\n Optical imagery refers to images captured by
\n\tsensors that detect visible light. It provides detailed information
\n\tabout the Earth’s surface, including colors and textures. <\/p>\n
Orthoready Data:
\n Orthoready Data refers to geospatial data
\n\tthat has undergone orthorectification, making it ready for analysis
\n\tand interpretation.<\/p>\n
Orthorectification:
\n Orthorectification is the process of
\n\tremoving geometric distortions from satellite or aerial images to
\n\tmake them accurate representations of the Earth’s surface. It
\n\tinvolves correcting for terrain variations and sensor effects. <\/p>\n
Pan sharpen natural color (PSNC):
\n Pan sharpening technique
\n\tapplied specifically to natural color imagery to enhance the spatial
\n\tresolution while preserving the natural color appearance. <\/p>\n
Pansharpened
\n : Pansharpening is a technique that combines
\n\thigh-resolution panchromatic imagery with lower-resolution
\n\tmultispectral imagery to create a single image with both high
\n\tspatial and spectral resolution. <\/p>\n
Polarization
\n : Polarization refers to the orientation of the
\n\telectric field vector of an electromagnetic wave. In SAR imagery,
\n\tdifferent polarization modes are used to enhance the interpretation
\n\tof surface features and materials. <\/p>\n
Radiometric correction:
\n Radiometric correction is the process
\n\tof adjusting satellite imagery to remove variations in brightness
\n\tand contrast caused by atmospheric conditions and sensor
\n\tcharacteristics. <\/p>\n
Raster:
\n Raster data is a type of geospatial data
\n\trepresentation that divides the Earth’s surface into a grid of cells
\n\tor pixels. Each pixel contains a value representing a specific
\n\tattribute or measurement. <\/p>\n
Remote Sensing:
\n It is the acquisition of information about an
\n\tobject or phenomenon without direct physical contact. It involves
\n\tthe use of satellites, aircraft, or other sensors to collect data,
\n\tsuch as imagery or spectral measurements, from a distance. <\/p>\n
Resolution:
\n Resolution refers to the level of detail or
\n\tclarity in an image or dataset. In EO satellites, it typically
\n\trepresents the smallest discernible ground feature that can be
\n\tcaptured. <\/p>\n
RF:
\n RF stands for Radio Frequency. In the context of
\n\tgeospatial technology, it refers to the use of radio waves for
\n\tcommunication or remote sensing applications. <\/p>\n
RGB:
\n RGB stands for Red, Green, Blue. It refers to the three
\n\tprimary colors used in combination to create a wide range of colors
\n\tin digital imagery. <\/p>\n
RMSE:
\n RMSE stands for Root Mean Square Error. It is a
\n\tstatistical measure used to quantify the difference between
\n\tpredicted and actual values. In geospatial analysis, RMSE can be
\n\tused to assess the accuracy of models or predictions. <\/p>\n
SAR Imagery:
\n SAR imagery refers to images captured by
\n\tSynthetic Aperture Radar (SAR) sensors. SAR uses radar signals to
\n\tcreate images and is particularly useful for mapping land features
\n\tand monitoring changes. <\/p>\n
SAR satellite
\n : Synthetic Aperture Radar (SAR) satellites use
\n\tradar technology to capture images of the Earth’s surface. SAR can
\n\tpenetrate clouds and capture data regardless of daylight or weather
\n\tconditions. <\/p>\n
Spatial accuracy:
\n Spatial accuracy refers to the correctness
\n\tor precision of the spatial information in geospatial data. It
\n\tmeasures how closely the data represents the actual geographic
\n\tlocations or features. <\/p>\n
Spatial Analysis:
\n It is the process of examining spatial data
\n\tto uncover patterns, relationships, and trends. Spatial analysis
\n\ttechniques include buffering, overlay, interpolation, and network
\n\tanalysis. <\/p>\n
Spatial Data Infrastructure (SDI):
\n It is a framework of
\n\ttechnologies, policies, and standards for managing and sharing
\n\tgeospatial data at different levels, from local to national or
\n\tglobal. SDIs promote data interoperability, facilitate data
\n\tdiscovery, and support decision-making processes across multiple
\n\torganizations.<\/p>\n
Spatial Data
\n : It refers to any data that is related to a
\n\tspecific location on the Earth’s surface. Spatial data can be
\n\trepresented as points, lines, polygons, or raster grids.<\/p>\n
Spatial Database: It is a database that is optimized for
\n\tstoring and querying spatial data. Spatial databases support spatial
\n\tindexing, spatial functions, and spatial queries to efficiently
\n\tmanage and retrieve spatial information. <\/p>\n
Spatial Join:
\n It is a geospatial operation that combines
\n\tattributes from two different spatial datasets based on their
\n\tspatial relationship. For example, a spatial join can match points
\n\twithin a polygon or identify the nearest neighbor for each feature
\n\tin a dataset. <\/p>\n
Spatial Query:
\n A Spatial Query is a search or analysis
\n\toperation that retrieves geospatial data based on specific spatial
\n\trelationships or criteria. It allows users to extract information
\n\tfrom a geospatial dataset based on its spatial attributes. <\/p>\n
Spatial Resolution:
\n It refers to the level of detail or the
\n\tsize of the smallest discernible feature in a geospatial dataset.
\n\tHigher spatial resolution means more detailed data. <\/p>\n
Sun Angle:
\n Sun Angle refers to the angle between the
\n\tdirection of the Sun and the Earth’s surface at a particular
\n\tlocation. It affects the illumination and shadowing of objects in
\n\tsatellite imagery. <\/p>\n
SWIR:
\n SWIR stands for Short-Wave Infrared. It refers to the
\n\tportion of the electromagnetic spectrum that lies between the NIR
\n\tand mid-wave infrared (MWIR) ranges. SWIR imagery is used for
\n\tvarious applications, including mineral exploration and moisture
\n\tdetection. <\/p>\n
Terrain:
\n Terrain refers to the physical characteristics and
\n\tfeatures of the Earth’s surface, including elevation, slope, and
\n\tlandforms. <\/p>\n
Topology
\n : Topology refers to the spatial relationships and
\n\tconnectivity between geographic features. It defines how points,
\n\tlines, and polygons relate to one another in a geospatial dataset. <\/p>\n
Vector:
\n Vector data is a type of geospatial data
\n\trepresentation that uses points, lines, and polygons to represent
\n\tgeographic features. It stores coordinates and attribute information
\n\tfor each feature. <\/p>\n
Wavelength:
\n Wavelength refers to the distance between two
\n\tsuccessive points of a wave. In geospatial technology, different
\n\twavelengths of electromagnetic radiation are used to capture and
\n\tanalyze data from the Earth’s surface. <\/p>\n
Web Mapping:
\n It refers to the process of creating,
\n\tpublishing, and interacting with maps on the internet. Web mapping
\n\tallows users to access and analyze geospatial data through web
\n\tbrowsers.<\/p>\n
FIle Formats<\/p>\n
Shapefile (.shp): Shapefile is a popular vector data format
\n\tdeveloped by Esri. It stores both geometry (points, lines, polygons)
\n\tand attribute data in multiple associated files with extensions such
\n\tas .shp, .shx, and .dbf. <\/p>\n
GeoJSON (.geojson):
\n GeoJSON is a format for encoding
\n\tgeospatial data in JSON (JavaScript Object Notation) format. It is
\n\twidely used for storing and exchanging vector data. <\/p>\n
KML\/KMZ:
\n KML (Keyhole Markup Language) and KMZ (compressed
\n\tKML) are file formats used for displaying geospatial data in
\n\tapplications like Google Earth. They can contain both vector and
\n\traster data, including points, lines, polygons, and overlays. <\/p>\n
GeoTIFF (.tif):
\n GeoTIFF is a georeferenced raster file format
\n\tthat incorporates geographic metadata. It allows storing satellite
\n\timagery, aerial photos, and other types of raster data with spatial
\n\treferencing. <\/p>\n
NetCDF (.nc)
\n : NetCDF (Network Common Data Form) is a
\n\tself-describing, machine-independent format commonly used for
\n\tstoring multidimensional geospatial data, such as climate data,
\n\tsatellite imagery, and oceanographic data. <\/p>\n
GML
\n (Geography Markup Language): GML is an XML-based file
\n\tformat for expressing geospatial features, including geometry,
\n\tattributes, and spatial relationships. It is used for data exchange
\n\tbetween different GIS software systems. <\/p>\n
Esri File Geodatabase (.gdb)
\n : File Geodatabase is a
\n\tproprietary file format developed by Esri for storing geospatial
\n\tdata in a structured database container. It supports the storage of
\n\tvarious vector and raster datasets. <\/p>\n
GeoPackage (.gpkg)
\n : GeoPackage is an open standard spatial
\n\tdatabase format designed for sharing geospatial data across
\n\tdifferent platforms. It supports both vector and raster data and can
\n\tstore multiple layers and attribute tables. <\/p>\n
ASCII Grid
\n : ASCII Grid is a text-based format for
\n\trepresenting gridded raster data. It stores elevation or attribute
\n\tvalues in a plain text file with a grid structure. <\/p>\n
LAS\/LAZ
\n : LAS (LiDAR Data Exchange Format) and LAZ (compressed
\n\tLAS) are file formats specifically designed for storing LiDAR point
\n\tcloud data. They store point coordinates, attributes, and
\n\tclassification information. <\/p>\n
MBTiles
\n : MBTiles is a format for storing tiled map data in a
\n\tSQLite database. It is commonly used for delivering map tiles in web
\n\tmapping applications. S57 <\/p>\n
ENC:
\n S57 ENC (Electronic Navigational Chart) is an
\n\tinternational standard format for storing and distributing
\n\telectronic navigational charts used in marine navigation systems.<\/p>\n
NITF
\n stands for National Imagery Transmission Format. It is a
\n\tfile format used for storing and transmitting geospatial and imagery
\n\tintelligence data, particularly in the defense and intelligence
\n\tcommunities. NITF was developed by the National
\n\tGeospatial-Intelligence Agency (NGA) of the United States. <\/p>\n
These are just a few key terms in the field of geospatial technology.
\nThere are many more specialized terms and concepts depending on the
\nspecific applications and disciplines within the geospatial domain.<\/p>\n","protected":false},"excerpt":{"rendered":"
Glossary of terms commonly used in geospatial technology Here is a glossary of terms commonly used in geospatial technology: AIS: Automatic Identification System (AIS) is a tracking system used in maritime applications. It uses VHF radio signals to collect and transmit vessel information such as position, speed, and identification. Analytic ready Data (ARD): Analytic Ready […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":14,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"yoast_head":"\n