Address Geocoding Software Market Outlook

According to our latest research, the global Address Geocoding Software market size was valued at USD 1.87 billion in 2024, and it is anticipated to grow at a robust CAGR of 12.1% from 2025 to 2033. By the end of 2033, the market is projected to reach USD 5.23 billion. This significant growth is driven by the increasing demand for precise location-based services across various sectors and the rapid adoption of advanced geospatial analytics solutions. The expanding use of geocoding technology in logistics, retail, and emergency response, as well as the proliferation of cloud-based deployment models, are key factors powering the market’s upward trajectory as per our latest research.

The primary growth driver for the Address Geocoding Software market is the escalating need for accurate and real-time geospatial data in business operations. Organizations across industries such as retail, logistics, and banking are leveraging geocoding software to enhance operational efficiency, optimize delivery routes, and improve customer experiences. With the surge in e-commerce and the growing importance of location intelligence in marketing, there is a marked increase in the adoption of geocoding solutions. Additionally, the integration of geocoding software with advanced technologies such as artificial intelligence and machine learning is enabling businesses to derive actionable insights from complex datasets, further fueling market growth.

Another significant factor contributing to the market’s expansion is the shift towards cloud-based deployment models. Cloud-based address geocoding solutions offer scalability, flexibility, and cost-effectiveness, making them attractive to both large enterprises and small and medium-sized businesses. The ability to access geocoding services remotely and integrate them seamlessly with other cloud-based applications is driving widespread adoption. Cloud deployment also supports real-time data processing and analytics, which is crucial for applications such as emergency response, fleet management, and dynamic mapping. As organizations increasingly prioritize digital transformation, the demand for cloud-enabled geocoding solutions is expected to rise substantially.

Moreover, the Address Geocoding Software market is benefiting from growing investments in smart city initiatives and urban planning projects worldwide. Governments and municipal authorities are deploying geocoding software to support infrastructure development, optimize public transportation, and enhance emergency response capabilities. The integration of geospatial analytics into urban planning is enabling more efficient resource allocation and improved citizen services. Additionally, the proliferation of Internet of Things (IoT) devices and the increasing use of connected vehicles are generating vast amounts of location data, which further boosts the need for sophisticated geocoding solutions.

From a regional perspective, North America currently holds the largest share of the Address Geocoding Software market, followed closely by Europe and the Asia Pacific. North America’s dominance is attributed to the high concentration of technology-driven enterprises, strong adoption of location-based services, and significant investments in geospatial analytics. The Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding e-commerce sectors, and increasing government initiatives to develop smart infrastructure. Meanwhile, Europe continues to demonstrate steady growth, supported by stringent data privacy regulations and the widespread use of advanced mapping technologies in transportation and logistics. The Middle East & Africa and Latin America are also emerging as promising markets, fueled by infrastructural development and the growing adoption of digital technologies.

Component Analysis

The Address Geocoding Software market is segmented by component into Software and Services, each playing a pivotal role in shaping the market’s landscape. The software segment is driven by the increasing demand for standalone geocoding applications and integrated solutions that can be embedded into existing enterprise systems. These software solutions are designed to convert address data into geographic coordinates, enabling organizations to leverage accurate location information for a wide range of applications, from logistics optimization to t

Geocoding AI Market Outlook

According to our latest research, the global Geocoding AI market size reached USD 2.18 billion in 2024 and is projected to grow at a robust CAGR of 15.7% through the forecast period, reaching USD 7.21 billion by 2033. This significant expansion is fueled by the rapid digitization of spatial data, the proliferation of location-based services, and the increasing adoption of artificial intelligence to enhance real-time geospatial analytics and decision-making. The demand for precise geocoding solutions is being driven by industries such as transportation, urban planning, and retail, where accurate location intelligence is paramount for operational efficiency and strategic growth.

One of the primary growth factors for the Geocoding AI market is the surge in demand for location-based services across various industries. With the rise of connected devices and the Internet of Things (IoT), enterprises are leveraging geocoding AI to provide real-time, context-aware services to their customers. For instance, in the retail and e-commerce sector, businesses use geocoding AI to optimize last-mile delivery, personalize marketing campaigns, and enhance customer experiences by offering location-specific recommendations. Additionally, the increasing integration of geocoding solutions into mobile applications and smart city frameworks is driving further adoption, as organizations seek to harness accurate geospatial data for better planning and resource allocation.

Another significant driver of market growth is the advancement in artificial intelligence and machine learning algorithms, which have substantially improved the accuracy, scalability, and efficiency of geocoding processes. Modern Geocoding AI solutions can process vast volumes of unstructured address data, correct errors, and provide precise latitude and longitude coordinates in real time. This technological progress is particularly beneficial for sectors like transportation and logistics, where route optimization and fleet management rely heavily on accurate geospatial information. Furthermore, the growing need for emergency management and disaster response has highlighted the importance of reliable geocoding AI to ensure rapid and effective deployment of resources in critical situations.

The third key factor propelling the Geocoding AI market is the increasing adoption of cloud-based deployment models. Cloud platforms offer scalability, flexibility, and cost-effectiveness, enabling organizations of all sizes to implement advanced geocoding solutions without significant upfront investments in infrastructure. The shift towards cloud-based geocoding AI is also facilitating seamless integration with other enterprise applications, enhancing data interoperability, and supporting remote and distributed workforces. As a result, small and medium enterprises (SMEs), which previously faced barriers to entry due to high costs and technical complexities, are now able to leverage sophisticated geocoding capabilities to compete more effectively in the digital economy.

From a regional perspective, North America currently dominates the Geocoding AI market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of major technology providers, high adoption rates of advanced analytics, and well-established digital infrastructure are key contributors to North America’s leadership. Meanwhile, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, expanding e-commerce, and significant investments in smart city initiatives. Latin America and the Middle East & Africa are also showing promising growth potential, supported by increasing government initiatives to improve urban planning and disaster management capabilities.

Component Analysis

The Component segment of the Geocoding AI market is bifurcated into Software and Services, each playing a pivotal role in the overall ecosystem. Geocoding AI software encompasses the core platforms and applications that process, standardize, and translate address data into geographic coordinates. These software solutions are continually evolving, integrating advanced AI and machine learning capabilities to enhance accuracy, speed, and user experience. The software segment is witnessing substantial investment from both established technology giants and innovative startups, resulting in a dynamic and competitive landscape. Customization, scalability, and ease of inte

This geocoding service provides the ability to perform tabular geocoding, reverse geocoding, and identifying results for locations that contain sub-addresses. This service and the supporting data are provided by the AddressNC program.A geocoding locator file is also available for users of ArcGIS Pro or ArcGIS Desktop in an offline/disconnected environment.

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the August 2025 release

• Nationally, the August 2025 update of G-NAF shows an overall increase of 40,716 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,794,643 of which 14,950,491 or 94.66% are principal.

• In the ACT, there have been minor updates to the address parsing of flat-numbered addresses aimed at: improving the address representation of flat-numbered addresses; improving address coverage; and improving address alignment between contributors. This change affects approximately 4,000 addresses.

• A small number of additional address sites have implemented the use of the BUILDING_NAME attribute as part of the merge criteria to improve address coverage for flat-numbered addresses in NSW and QLD. These changes have resulted in the creation of approximately 400 addresses in NSW and 120 in QLD.

• A focus has been applied to Tasmanian street-locality addresses to reduce the number of these addresses. For the August 2025 release, there is a reduction of some 900 street-locality addresses in Tasmania.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

Размер мирового рынка аналитики местоположения был оценен в 10,34 млрд. Долл. США в 2023 году и, как ожидается, вырастет на 17,5% с 2023 по 2030 год, чтобы достичь 33,13 млрд долларов. Размер рынка, рост, доля

Global Location Analytics Software market size 2025 was XX Million. Location Analytics Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Address ranges describe a label given to a unique collection of addresses that fall along a road or path. Address ranges provide a way of locating homes and businesses based on their street addresses when no other location information is available.Using a house number, street name, street side and ZIP code, address ranges can locate the address to the geographic area associated to that side of the street. Once geocoded, the U.S. Census Bureau can assign the address to a field assignment area or tabulate the data for that address. In addition, academics, researchers, professionals and government agencies outside of the Census Bureau use MAF/TIGER address ranges to transform tabular addresses into geographical datasets for decision-making and analytical purposes.Address ranges must be unique to geocode addresses to the correct location and avoid geocoding conflicts. Multiple elements in MAF/TIGER are required to make an address range unique including street names, address house numbers and street feature geometries, such as street centerlines. The address range data model is designed to maximize geocoding matches with their correct geographic areas in MAF/TIGER by allowing an unlimited number of address range-to-street feature relationships.The Census Bureau’s Geography Division devises numerous operations and processes to build and maintain high quality address ranges so that:Address ranges accurately describe the location of addresses on the ground.Address All possible city-style addresses are geocoded.Address ranges can handle all known address and street name variations.Address ranges conform with current U.S. Postal Service ZIP codes.Address ranges are reliable and free from conflicts.Automated software continually updates existing address ranges, builds new address ranges and corrects errors. An automated operation links address location points and tabular address information to street feature edges with matching street names in the same block to build and modify address ranges.Many business rules and legal value checks ensure quality address range data in MAF/TIGER. For example, business rules prevent adding or modifying address ranges that overlap another house number range with the same street name and ZIP code. Legal value checks verify that address ranges include mandatory attribute information, valid data types and valid character values.Some of the TIGER/Line products for the public include address ranges and give the public the ability to geocode addresses to MAF/TIGER address ranges for the user’s own purpose. The address range files are available for the nation, Puerto Rico and the U.S. Island Areas at the county level. TIGER/Line files require geographic information system (GIS) software to use.The Census Bureau Geocoder Service is a web service provided to the public. The service accepts up to 1,000 input addresses and, based on Census address ranges, returns the interpolated geocoded location and census geographies. Users can access the service a web interface or a representational state transfer (REST) application program interface (API) web service. See the Census Geocoder for more information on this process. Directions on how to use the Census Geocoder available: Geocoding Services Web Application Programming Interface (API)Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_addr.gdb.zip

Point geometry feature class representing known regulated health facilities in Colorado, developed directly from address information obtained directly from the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment in January of 2017. Coordinate locations (latitude/longitude) were derived from the known street addresses of health facilities using a combination of Centrus MapMarker geocoding software and Google Earth imagery locations. This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment.

The GeoPinpoint Suite software attaches geographic coordinates to records in a client database by means of matching certain database fields against a DMTI proprietary geo-reference database. The geo- reference database is comprised of digital street geometry, street address ranges, postal coordinates, point of interest and other reference databases to ensure that data is “geocoded” as accurately as possible. When data is “geocoded”, co-ordinates can be transferred into a Geographic Information Systems (GIS) such as MapInfo, ArcInfo, ArcView and other software systems that support the importation of geographic co-ordinate locations. GeoPinpointTM Suite positions your data using a powerful and innovative geo-location process called geocoding. GeoPinpoint Suite attaches X and Y coordinates to your facility, customer or prospect address data for map visualization, analysis or location based applications. The GeoPinpoint Suite takes advantage of a new modular design that allows the software to encompass future module enhancements without jeopardizing its performance or usability. Based on the nationwide precision and the robust street address content of CanMap® Streetfiles, GeoPinpoint Suite has been engineered to geocode your data with a high degree of accuracy.

Global Address Verification Software is segmented by Application (E-commerce, Logistics, Banking, Government, Utilities), Type (Batch Verification, Real-Time API, Geocoding Software, Address Autocomplete, International Address Validation) and Geography(North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

Location Intelligence Market is Segmented by Component (Software and Services), Solution Type (Geocoding and Reverse, Geocoding, and More), Location Type (Indoor and Outdoor), Deployment (Cloud and On-Premise), Application (Workforce Management, Asset Management and More), End-User Vertical (Retail and Consumer Goods, Government and Defense, and More) and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

AddressNC has been prioritized by the North Carolina Geographic Information Coordinating Council (GICC) as a critical framework dataset. The AddressNC Program runs parallel to and is derived from the North Carolina 911 Board Next Generation 911 (NG911) Program.  Address data has been identified as mission critical for validation and accurate call routing within NG911 and the AddressNC Program completes a full-circle approach of address maintenance and sustainability through applied enhancements and quality control beyond 911 requirements.  A primary goal of AddressNC is to continually develop and maintain quality address points on a continuous cycle through updates published in NG911. Various agencies in federal, state, and local government can benefit by applying practical applications of quality addressing in their own programs, negating the need to rely on outdated statewide addressing data and/or using paid address data sets from third party sources.

A Feature web service of the Address Point file of buildings and properties in New York State. Please note that, due to the large size, the NYS Address Point statewide layer cannot be downloaded in shapefile format. A map service of the Street and Address Maintenance (SAM) Program Address Point file is available here: https://gisservices.its.ny.gov/arcgis/rest/services.SAM Address Points Data Dictionary: https://gis.ny.gov/system/files/documents/2024/02/address-points-data-dictionary.pdf. If the purpose of accessing the address points service is for geocoding, NYS ITS has a publicly available geocoding service which includes the address points along with other layers. For more information about the geocoding service, please visit: https://gis.ny.gov/address-geocoder. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions. Publication Date: See Update Frequency. Current as of Date: 2 business days prior to Publication Date. Update frequency: Second and fourth Friday of each month. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.This feature service is available to the public.

A Feature web service of the Address Point file of buildings and properties in New York State. Please note that, due to the large size, the NYS Address Point statewide layer cannot be downloaded in shapefile format. A map service of the Street and Address Maintenance (SAM) Program Address Point file is available here: https://gisservices.its.ny.gov/arcgis/rest/services.SAM Address Points Data Dictionary: https://gis.ny.gov/system/files/documents/2024/02/address-points-data-dictionary.pdf. If the purpose of accessing the address points service is for geocoding, NYS ITS has a publicly available geocoding service which includes the address points along with other layers. For more information about the geocoding service, please visit: https://gis.ny.gov/address-geocoder. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions. Publication Date: See Update Frequency. Current as of Date: 2 business days prior to Publication Date. Update frequency: Second and fourth Friday of each month. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.This feature service is available to the public.

AddressNC has been prioritized by the North Carolina Geographic Information Coordinating Council (GICC) as a critical framework dataset. The AddressNC Program runs parallel to and is derived from the North Carolina 911 Board Next Generation 911 (NG911) Program.  Address data has been identified as mission critical for validation and accurate call routing within NG911 and the AddressNC Program completes a full-circle approach of address maintenance and sustainability through applied enhancements and quality control beyond 911 requirements.  A primary goal of AddressNC is to continually develop and maintain quality address points on a continuous cycle through updates published in NG911. Various agencies in federal, state, and local government can benefit by applying practical applications of quality addressing in their own programs, negating the need to rely on outdated statewide addressing data and/or using paid address data sets from third party sources.

Point geometry feature class representing Community Behavioral Health Centers and Resource locations, developed directly from address information obtained directly from the Office of Emergency Preparedness and Response, Colorado Department of Public Health and Environment, in 2017. Coordinate locations (latitude/longitude) were derived from the known street addresses of health facilities using a combination of Centrus MapMarker geocoding software and Google Earth imagery locations. This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment.

Point geometry feature class representing known Colorado Drug Treatment Program Resource locations (Methadone Clinics and Narcotic Addiction Treatment Programs), developed directly from address information obtained directly from the Office of Emergency Preparedness and Response, Colorado Department of Public Health and Environment, in 2017. Coordinate locations (latitude/longitude) were derived from the known street addresses of health facilities using a combination of Centrus MapMarker geocoding software and Google Earth imagery locations. This file was developed for use in activities and exercises within the Colorado Department of Public Health and Environment.

Point geometry feature class representing local Colorado public health agency locations and satellite offices obtained directly from data organized by the Colorado Department of Public Health and Environment in 2018. Coordinate locations (latitude/longitude) were derived from the known street addresses of health facilities using a combination of Centrus MapMarker geocoding software and Google Earth imagery locations. This file was developed by the Colorado Department of Public Health and Environment for use in program activities and emergency preparedness exercises.

SummaryThis data set shows building permits for the Baltimore metropolitan region. The data goes back to 2000 and is updated approximately once every two months. Expanded building permit data can be found at https://www.baltometro.org/community/data-maps/building-permit-data. Description The permits include any permit that is use code 40-48 (most new residential), 60-65 (mixed use), or is greater than or equal to $50,000. Historically, BMC receives the permits from participating jurisdictions and geocodes them. In recent years, some jurisdictions have started geocoding their own permits. When this is the case, BMC incorporates the geocoded points as given, and does not include them in its own geocoding process.Expanded building permit data can be found at https://www.baltometro.org/community/data-maps/building-permit-data.Layers:BPDS_Residential_New_ConstructionBPDS_Residential_AlterationsBPDS_Non_Residential_New_ConstructionBPDS_ Non_Residential _AlterationsBPDS_Mixed_Use_New_ConstructionThere is no layer for Mixed Use alterations; alterations to Mixed Use always get classified as Residential or Non-Residential. Field Names Field Name (alias)Descriptionpermit_no (County Permit ID)Original permit ID provided by the jurisdiction issue_dt (Date Permit Was Issued)Date the permit was issuedxcoord (X Coordinate)Longitude, in NAD 1983 decimal degreesycoord (Y Coordinate)Latitude, in NAD 1983 decimal degreessite_addr (Site Address)Address of the constructionzipcode (Site Zipcode)Zipcode of the constructionoffice (Office Number)This number corresponds to a jurisdiction and is used for BMC administrative recordspmt_use (Permit Use)Permit use code. A list of the values can be found at https://gis.baltometro.org/Application/BPDS/docs/BPDS_Permit_Use_Codes.pdfpmt_type (Permit Type)Permit type code. A list of the values can be found at https://gis.baltometro.org/Application/BPDS/docs/BPDS_Permit_Use_Codes.pdfdevelopment_name (Development Name / Subdivision)Subdivision name, if providedunit_count (Number of Units)Number of units, if provided. Only found in residential recordstenure (Tenure)If provided, indicates whether building is expected to be for rent or for sale after construction is complete. 1=For Rent, 2=For Saleamount (Amount)Estimated cost of constructionpmt_cat (Permit Category)Simplified classification of the pmt_use and pmt_type fieldsdescrip (Description)Description of construction, if providedJurisdiction (Jurisdiction)Jurisdiction (a county or city) Update CycleThe data is updated approximately once every three months. User Note Over the years, building permit points were geocoded using a variety of software and reference data. The Baltimore Metropolitan Council made every effort to ensure accurate geocoding however there may be inaccuracies or inconsistencies in how the points were placed. For best results, the Baltimore Metropolitan Council recommends aggregating the building permit points to a larger geography (ex. Census tract, zip code) when analyzing the data.Data Access Instructions To download the data or access it via API, visit https://gisdata.baltometro.org/. Technical ContactFor questions or comments, contact Erin Bolton, GIS Coordinator, at ebolton@baltometro.org or 410-732-0500.

The NRSC's geocoding service allows SAR scenes from potentially any SAR sensor to be spheroid and terrain corrected. This puts the image into the map co-ordinates of a specified projection. A wide range of projections can be catered for. The geocoded version of the scene has the appearance of being taken vertically (as if the sensor is pointing down directly to the earth's surface instead of viewing it from an angle) which makes interpretation and further processing of the data easier and more natural.

In addition to the SAR images, a digital elevation model (DEM/DTM) of the area is required, preferably at 100m or better resolution. If a DEM is unavailable, a spheroid-corrected but not terrain-corrected image can still be produced.

The process can either be provided for supplied data or as part of a complete service of acquiring and processing imagery for requested areas/dates.

The T-SAR (Topographic SAR) software used to process the SAR imagery is capable of generating a number of additional products useful for interpreting the data, including:

Radiometrically Corrected Image (Calibrated Image): the radiometrically- corrected or calibrated image is a version of the SAR scene where the values of each pixel have been corrected for range (in the original SAR data pixels further from the sensor are dimmer), antenna pattern effects, and the effect of the angle of incidence between the radar beam and the earth's surface at the pixel.

Pixel Validity Mask (Mapped pixels, Shadow, Layover): this product identifies which SAR pixels are valid/invalid. Pixels may be invalid for several reasons. First, products based on the terrain will be invalid for areas that fall outside the DEM. Second, due to the physics of the SAR sensor, pixels that fall into areas of 'shadow' or 'layover' are invalid. A combined Pixel Validity Mask is available showing which pixels are valid after considering all three cases, or three separate masks can be generated showing the pixels invalidated by each effect, the Mapped Pixel Array (showing pixels which fall inside the DEM), the Layover Mask (showing areas of layover) and the Shadow Mask (showing areas of shadow).

Pixel Geoidal Heights: this is effectively the DEM transformed into the co-ordinate system of the original SAR image, i.e. the height of each SAR pixel is recorded.

Angles of Slope/Incidence: these two products respectively record the angle of slope of the earth's surface and the angle of incidence between the radar beam and the surface for each SAR pixel.

Projected Pixel Area: this product gives the area (in square metres) of the earth's surface contributing to each pixel in the SAR image.

These products can be generated in either the original SAR co-ordinates or map co-ordinates for a given projection (i.e. geocoded co-ordinates).

Finally, an Image to Map Look-up Table is available which gives the map co-ordinates of each pixel in the original SAR image.

The processed data can be supplied as either digital data (on Exabyte, CD- ROM or CCT), or in various hard-copy forms. Further details and prices for processing are available from the National Remote Sensing Centre (NRSC).