United Kingdom Exports of maps, hydrographic or similar charts (printed) to United States was US$19.82 Million during 2024, according to the United Nations COMTRADE database on international trade. United Kingdom Exports of maps, hydrographic or similar charts (printed) to United States - data, historical chart and statistics - was last updated on July of 2025.

World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain, TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Maxar's Precision3D covering parts of the globe.Topography sources listed in the table below are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only. Data Source Native Pixel Size Approximate Pixel Size (meters) Coverage Primary Source Country/Region

Topography

Australia 1m 1 meter 1 Partial areas of Australia Geoscience Australia Australia

Moreton Bay, Australia 1m 1 meter 1 Moreton Bay region, Australia Moreton Bay Regional Council Australia

New South Wales, Australia 5m 5 meters 5 New South Wales State, Australia DFSI Australia

SRTM 1 arc second DEM-S 0.0002777777777779 degrees 31 Australia Geoscience Australia Australia

Burgenland 50cm 0.5 meters 0.5 Burgenland State, Austria Land Burgenland Austria

Upper Austria 50cm 0.5 meters 0.5 Upper Austria State, Austria Land Oberosterreich Austria

Austria 1m 1 meter 1 Austria BEV Austria

Austria 10m 10 meters 10 Austria BEV Austria

Wallonie 50cm 0.5 meters 0.5 Wallonie state, Belgium Service public de Wallonie (SPW) Belgium

Vlaanderen 1m 1 meter 1 Vlaanderen state, Belgium agentschap Digitaal Vlaanderen Belgium

Canada HRDEM 1m 1 meter 1 Partial areas of Canada Natural Resources Canada Canada

Canada HRDEM 2m 2 meter 2 Partial areas of the southern part of Canada Natural Resources Canada Canada

Denmark 40cm 0.4 meters 0.4 Denmark KDS Denmark

Denmark 10m 10 meters 10 Denmark KDS Denmark

England 1m 1 meter 1 England Environment Agency England

Estonia 1m 1 meter 1 Estonia Estonian Land Board Estonia

Estonia 5m 5 meters 5 Estonia Estonian Land Board Estonia

Estonia 10m 10 meters 10 Estonia Estonian Land Board Estonia

Finland 2m 2 meters 2 Finland NLS Finland

Finland 10m 10 meters 10 Finland NLS Finland

France 1m 1 meter 1 France IGN-F France

Bavaria 1m 1 meter 1 Bavaria State, Germany Bayerische Vermessungsverwaltung Germany

Berlin 1m 1 meter 1 Berlin State, Germany Geoportal Berlin Germany

Brandenburg 1m 1 meter 1 Brandenburg State, Germany GeoBasis-DE/LGB Germany

Hamburg 1m 1 meter 1 Hamburg State, Germany LGV Hamburg Germany

Hesse 1m 1 meter 1 Hesse State, Germany HVBG Germany

Nordrhein-Westfalen 1m 1 meter 1 Nordrhein-Westfalen State, Germany Land NRW Germany

Saxony 1m 1 meter 1 Saxony State, Germany Landesamt für Geobasisinformation Sachsen (GeoSN) Germany

Sachsen-Anhalt 2m 2 meters 2 Sachsen-Anhalt State, Germany LVermGeo LSA Germany

Hong Kong 50cm 0.5 meters 0.5 Hong Kong CEDD Hong Kong SAR

Italy TINITALY 10m 10 meters 10 Italy INGV Italy

Japan DEM5A *, DEM5B * 0.000055555555 degrees 5 Partial areas of Japan GSI Japan

Japan DEM10B * 0.00011111111 degrees 10 Japan GSI Japan

Latvia 1m 1 meters 1 Latvia Latvian Geospatial Information Agency Latvia

Latvia 10m 10 meters 10 Latvia Latvian Geospatial Information Agency Latvia

Latvia 20m 20 meters 20 Latvia Latvian Geospatial Information Agency Latvia

Lithuania 1m 1 meters 1 Lithuania NZT Lithuania

Lithuania 10m 10 meters 10 Lithuania NZT Lithuania

Netherlands (AHN3/AHN4) 50cm 0.5 meters 0.5 Netherlands AHN Netherlands

Netherlands (AHN3/AHN4) 10m 10 meters 10 Netherlands AHN Netherlands

New Zealand 1m 1 meters 1 Partial areas of New Zealand Land Information New Zealand (Sourced from LINZ. CC BY 4.0) New Zealand

Northern Ireland 10m 10 meters 10 Northern Ireland OSNI Northern Ireland

Norway 10m 10 meters 10 Norway NMA Norway

Poland 1m 1 meter 1 Partial areas of Poland GUGIK Poland

Poland 5m 5 meters 5 Partial areas of Poland GUGIK Poland

Scotland 1m 1 meter 1 Partial areas of Scotland Scottish Government et.al Scotland

Slovakia 1m 1 meter 1 Slovakia ÚGKK SR Slovakia

Slovakia 10m 10 meters 10 Slovakia GKÚ Slovakia

Slovenia 1m 1 meter 1 Slovenia ARSO Slovenia

Madrid City 1m 1 meter 1 Madrid city, Spain Ayuntamiento de Madrid Spain

Spain 2m (MDT02 2019 CC-BY 4.0 scne.es) 2 meters 2 Partial areas of Spain IGN Spain

Spain 5m 5 meters 5 Spain IGN Spain

Spain 10m 10 meters 10 Spain IGN Spain

Varnamo 50cm 0.5 meters 0.5 Varnamo municipality, Sweden Värnamo Kommun Sweden

Canton of Basel-Landschaft 25cm 0.25 meters 0.25 Canton of Basel-Landschaft, Switzerland Geoinformation Kanton Basel-Landschaft Switzerland

Grand Geneva 50cm 0.5 meters 0.5 Grand Geneva metropolitan, France/Switzerland SITG Switzerland and France

Switzerland swissALTI3D 50cm 0.5 meters 0.5 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

Switzerland swissALTI3D 10m 10 meters 10 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

OS Terrain 50 50 meters 50 United Kingdom Ordnance Survey United Kingdom

Douglas County 1ft 1 foot 0.3048 Douglas County, Nebraska, USA Douglas County NE United States

Lancaster County 1ft 1 foot 0.3048 Lancaster County, Nebraska, USA Lancaster County NE United States

Sarpy County 1ft 1 foot 0.3048 Sarpy County, Nebraska, USA Sarpy County NE United States

Cook County 1.5 ft 1.5 foot 0.46 Cook County, Illinois, USA ISGS United States

3DEP 1m 1 meter 1 Partial areas of the conterminous United States, Puerto Rico USGS United States

NRCS 1m 1 meter 1 Partial areas of the conterminous United States NRCS USDA United States

San Mateo County 1m 1 meter 1 San Mateo County, California, USA San Mateo County CA United States

FEMA LiDAR DTM 3 meters 3 Partial areas of the conterminous United States FEMA United States

NED 1/9 arc second 0.000030864197530866 degrees 3 Partial areas of the conterminous United States USGS United States

3DEP 5m 5 meter 5 Alaska, United States USGS United States

NED 1/3 arc second 0.000092592592593 degrees 10 conterminous United States, Hawaii, Alaska, Puerto Rico, and Territorial Islands of the United States USGS United States

NED 1 arc second 0.0002777777777779 degrees 31 conterminous United States, Hawaii, Alaska, Puerto Rico, Territorial Islands of the United States; Canada and Mexico USGS United States

NED 2 arc second 0.000555555555556 degrees 62 Alaska, United States USGS United States

Wales 1m 1 meter 1 Wales Welsh Government Wales

WorldDEM4Ortho 0.00022222222 degrees 24 Global (excluding the countries of Azerbaijan, DR Congo and Ukraine) Airbus Defense and Space GmbH World

SRTM 1 arc second 0.0002777777777779 degrees 31 all land areas between 60 degrees north and 56 degrees south except Australia NASA World

EarthEnv-DEM90 0.00083333333333333 degrees 93 Global N Robinson,NCEAS World

SRTM v4.1 0.00083333333333333 degrees 93 all land areas between 60 degrees north and 56 degrees south except Australia CGIAR-CSI World

GMTED2010 7.5 arc second 0.00208333333333333 degrees 232 Global USGS World

GMTED2010 15 arc second 0.00416666666666666 degrees 464 Global USGS World

GMTED2010 30 arc second 0.0083333333333333 degrees 928 Global USGS World

Bathymetry

Bass Strait 30m 2022 0.0003 degrees 30 area of seabed between the coastlines of Victoria and northern Tasmania, extending approximately 460 km from west of King Island to east of Flinders Island. Geoscience Australia Australia

AusBathyTopo 2024 0.0025 degrees 250 Australian continent and Tasmania, and surrounding Macquarie Island and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands. Geoscience Australia Australia

Canada west coast 10 meters 10 Canada west coast Natural Resources Canada Canada

Gulf of Mexico 40 feet 12 Northern Gulf of Mexico BOEM Gulf of Mexico

MH370 150 meters 150 MH370 flight search area (Phase 1) of Indian Ocean Geoscience Australia Indian Ocean

Switzerland swissBATHY3D 1 - 3 meters 1, 2, 3 Lakes of Switzerland swisstopo Switzerland

NCEI 1/9 arc second 0.000030864197530866 degrees 3 Puerto Rico, U.S Virgin Islands and partial areas of eastern and western United States coast NOAA NCEI United States

NCEI 1/3 arc second 0.000092592592593 degrees 10 Partial areas of eastern and western United States

United States Exports of maps, hydrographic or similar charts (printed) to United Kingdom was US$551.04 Thousand during 2024, according to the United Nations COMTRADE database on international trade. United States Exports of maps, hydrographic or similar charts (printed) to United Kingdom - data, historical chart and statistics - was last updated on June of 2025.

The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.

The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable.

Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes.

Datasets used: Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate Data

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Snapshot img

HD Map For Autonomous Vehicles Market Size 2024-2028

The HD map for autonomous vehicles market size is forecast to increase by USD 14 billion at a CAGR of 40.5% between 2023 and 2029

The market is experiencing significant growth, driven by the increasing adoption of autonomous vehicles and the development of advanced connected infrastructure. The integration of high-definition maps into autonomous systems enables vehicles to navigate complex environments more accurately and efficiently, reducing the risk of accidents and improving overall performance. HD map creation for autonomous vehicles is a complex process involving data acquisition, aggregation, and integration of advanced technologies such as AI and machine learning. However, the high cost associated with the technology remains a significant challenge for market expansion. Manufacturers must continue to innovate and find cost-effective solutions to make HD maps an essential component of autonomous vehicles, rather than a luxury. Companies seeking to capitalize on this market opportunity should focus on collaborating with infrastructure providers, developing scalable and cost-effective HD mapping technologies, and ensuring seamless integration with autonomous systems. By addressing these challenges and leveraging the growing demand for autonomous vehicles and advanced infrastructure, market participants can effectively navigate the strategic landscape and drive long-term success.

What will be the Size of the Market during the forecast period?

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The market is experiencing significant growth as the global push towards advanced driver-assistance systems (ADAS) and fully autonomous vehicles (AVs) continues. HD Maps, which utilize technologies such as Lidar, SLAM (Simultaneous Localization and Mapping), and digital cameras, play a crucial role in enabling AVs to navigate roads safely and efficiently. These maps provide real-time, high-precision data to AV systems, allowing them to identify and respond to road conditions, obstacles, and other vehicles in real time. The market is expected to reach a substantial size in the coming years, driven by the increasing demand for shared mobility services, including ride-sharing and robo-taxi services.
The integration of 5G networks is also expected to accelerate the adoption of HD Maps, as they enable faster and more reliable data transmission between vehicles and maps. The market is witnessing continuous innovation, with companies investing heavily in research and development to improve the accuracy and coverage of HD Maps. Additionally, the integration of HD Maps with other technologies, such as sensor fusion and deep learning algorithms, is expected to further enhance the capabilities of AVs. Overall, the HD Map market for autonomous vehicles is a dynamic and rapidly evolving market, poised for significant growth in the coming years.

How is this Industry segmented?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Solution

  Cloud-based
  Embedded


Vehicle Type

  Passenger
  Commercial


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. HD maps are a critical component in the advancement of autonomous vehicles. These high-definition maps offer enhanced accuracy and precision for navigation, while their cloud-based infrastructure ensures accessibility and ease of updates. This enables autonomous vehicles to navigate complex and unfamiliar environments more effectively. Notable industry players, such as NavInfo Co. Ltd. (Navinfo), HERE Global BV (HERE), TomTom NV (TomTom), and NVIDIA Corp. (NVIDIA), prioritize cloud-based solutions and real-time services for their HD mapping offerings. The integration of 5G networks further enhances the capabilities of HD maps, contributing to the growth of autonomous driving technology in passenger and commercial vehicles.

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The cloud-based segment was valued at USD 1047.3 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 40% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America is primarily driven by the United States, where the increasing deployment of

All organisations hold information about the core of their business. Forestry England holds information on trees and forests. We use this information to help us run our business and make decisions.

The role of the Forest Inventory (the Sub-compartment Database (SCDB) and the stock maps) is to be our authoritative data source, giving us information for recording, monitoring, analysis and reporting. Through this it supports decision-making on the whole of the FE estate. Information from the Inventory is used by FE, wider government, industry and the public for economic, environmental and social forest-related decision-making.

Furthermore, it supports forest-related national policy development and government initiatives, and helps us meet our national and international forest-related reporting responsibilities. Information on our current forest resource, and the future expansion and availability of wood products from our forests, is vital for planners both in and outside FE. It is used when looking at the development of processing industries, regional infrastructure, the effect upon communities of our actions, and to prepare and monitor government policies. The Inventory (SCDB and stock maps), with ‘Future Forest Structure’ and the ‘rollback’ functionality of Forester, will help provide a definitive measure of trends in extent, structure, composition, health, status, use, and management of all FE land holdings.

We require this to meet national and international commitments, to report on the sustainable management of forests as well as to help us through the process of business and Forest Design Planning. As well as helping with the above, the SCDB helps us address detailed requests from industry, government, non-government organisations and the public for information on our estate. FE's growing national and international responsibilities and the requirements for monitoring and reporting on a range of forest statistics have highlighted the technical challenges we face in providing consistent, national level data. A well kept and managed SCDB and GIS (Geographical Information System - Forester) will provide the best solution for this and assist countries in evidence-based policy making. Looking ahead at international reporting commitments; one example of an area where requirements look set to increase will be reporting on our work to combat climate change and how our estate contributes to carbon sequestration. We have put in place processes to ensure that at least the basics of our inventory are covered:

1. The inventory of forests;
2. The land-uses;
3. The land we own ( Deeds);
4. The roads we manage.

We depend on others to allow us to manage the forests and to provide us with funds and in doing so we need to be seen to be responsible and accountable for our actions. A foundation of achieving this is good record keeping. A subcompartment should be recognisable on the ground. It will be similar enough in land use, species or habitat composition, yield class, age, condition, thinning history etc. to be treated as a single unit. They will generally be contiguous in nature and will not be split by roads, rivers, open space etc. Distinct boundaries are required, and these will often change as crops are felled, thinned, replanted and resurveyed. In some parts of the country foresters used historical and topographical features to delineate subcompartment boundaries, such as hedges, walls and escarpments. In other areas no account of the history and topography of the site was taken, with field boundaries, hedges, walls, streams etc. being subsumed into the sub-compartment. Also, these features may or may not appear on the OS backdrop, again this was dependent on the staff involved and what they felt was relevant to the map. The main point is that, as managers we may find such obvious features in the middle of a subcompartment when nothing is indicated on the stock map, while the same thing would be indicated elsewhere.

Attributes;

FOREST Cost centre Nos. COMPTMENT Compartment Nos. SUBCOMPT Sub-compartment letter BLOCK Block nos. CULTCODE Cultivation Code CULTIVATN Cultivation PRIHABCODE Primary Habitat Code PRIHABITAT Primary Habitat PRILANDUSE Land Use of primary component PRISPECIES Primary component tree species PRI_PLYEAR prim. component year planted PRIPCTAREA Prim. component %Area of sub-compartment SECHABCODE Secondary Habitat Code SECHABITAT Secondary Habitat SECLANDUSE Land Use of secondary component SECSPECIES Secondary component tree species SEC_PLYEAR Secondary component year planted SECPCTAREA Secondary component %Area of sub-compartment TERLANDUSE Land Use of tertiary component TERSPECIES Tertiary component tree species TER_PLYEAR Tertiary component year planted TERPCTAREA Tertiary component %Area of sub-compartment TERHABITAT Tertiary Habitat TERHABCODE Tertiary Habitat Code.

Any maps produced using this data should contain the following Forestry Commission acknowledgement: “Contains, or is based on, information supplied by the Forestry Commission. © Crown copyright and database right 2025 Ordnance Survey AC0000814847”.

The 2020 TIGER/Line Shapefiles contain current geographic extent and boundaries of both legal and statistical entities (which have no governmental standing) for the United States, the District of Columbia, Puerto Rico, and the Island areas. This vintage includes boundaries of governmental units that match the data from the surveys that use 2020 geography (e.g., 2020 Population Estimates and the 2020 American Community Survey). In addition to geographic boundaries, the 2020 TIGER/Line Shapefiles also include geographic feature shapefiles and relationship files. Feature shapefiles represent the point, line and polygon features in the MTDB (e.g., roads and rivers). Relationship files contain additional attribute information users can join to the shapefiles. Both the feature shapefiles and relationship files reflect updates made in the database through September 2020. To see how the geographic entities, relate to one another, please see our geographic hierarchy diagrams here.Census Urbanized Areashttps://www2.census.gov/geo/tiger/TIGER2020/UACCensus Urban/Rural Census Block Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php2020 TIGER/Line and Redistricting shapefiles:https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.htmlTechnical documentation:https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdfTIGERweb REST Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.htmlThe legal entities included in these shapefiles are:American Indian Off-Reservation Trust LandsAmerican Indian Reservations – FederalAmerican Indian Reservations – StateAmerican Indian Tribal Subdivisions (within legal American Indian areas)Alaska Native Regional CorporationsCongressional Districts – 116th CongressConsolidated CitiesCounties and Equivalent Entities (except census areas in Alaska)Estates (US Virgin Islands only)Hawaiian Home LandsIncorporated PlacesMinor Civil DivisionsSchool Districts – ElementarySchool Districts – SecondarySchool Districts – UnifiedStates and Equivalent EntitiesState Legislative Districts – UpperState Legislative Districts – LowerSubminor Civil Divisions (Subbarrios in Puerto Rico)The statistical entities included in these shapefiles are:Alaska Native Village Statistical AreasAmerican Indian/Alaska Native Statistical AreasAmerican Indian Tribal Subdivisions (within Oklahoma Tribal Statistical Areas)Block Groups3-5Census AreasCensus BlocksCensus County Divisions (Census Subareas in Alaska)Unorganized Territories (statistical county subdivisions)Census Designated Places (CDPs)Census TractsCombined New England City and Town AreasCombined Statistical AreasMetropolitan and Micropolitan Statistical Areas and related statistical areasMetropolitan DivisionsNew England City and Town AreasNew England City and Town Area DivisionsOklahoma Tribal Statistical AreasPublic Use Microdata Areas (PUMAs)State Designated Tribal Statistical AreasTribal Designated Statistical AreasUrban AreasZIP Code Tabulation Areas (ZCTAs)Shapefiles - Features:Address Range-FeatureAll Lines (called Edges)All RoadsArea HydrographyArea LandmarkCoastlineLinear HydrographyMilitary InstallationPoint LandmarkPrimary RoadsPrimary and Secondary RoadsTopological Faces (polygons with all geocodes)Relationship Files:Address Range-Feature NameAddress RangesFeature NamesTopological Faces – Area LandmarkTopological Faces – Area HydrographyTopological Faces – Military Installations

Explore the interactive maps showing the average delay and average speed on the Strategic Road Network and local ‘A’ roads in England, in 2022.

On the Strategic Road Network (SRN) for 2022, the average delay is estimated to be 9.3 seconds per vehicle per mile (spvpm), compared to free flow, a 9.4% increase on 2021 and a 2.1% decrease on 2019.

The average speed is estimated to be 58.1 mph, down 1.4% from 2021 and up 0.2% from 2019.

On local ‘A’ roads for 2022, the average delay was estimated to be 45.5 seconds per vehicle per mile compared to free flow, up 2.5% from 2021 and down 2.8% from 2019 (pre-coronavirus)

The average speed is estimated to be 23.7 mph, down 1.7% from 2021 and up 2.2% from 2019 (pre-coronavirus).

Average speeds in 2022 have stabilised towards similar trends observed before the effects of the coronavirus pandemic.

The outbreak of coronavirus (COVID-19) has had a marked impact on everyday life, including on congestion on the road network. As some of these data are affected by the coronavirus pandemic in the UK, caution should be taken when interpreting these statistics and comparing them with other time periods. Additional http://bit.ly/COVID_Congestion_Analysis" class="govuk-link">analysis on the impact of the coronavirus pandemic on road journeys in 2020 is also available. This Storymap contains charts and interactive maps for road journeys in England in 2020.

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Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

Snapshot img

Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

The digital map market is estimated to capture a valuation of US$ 18.3 billion in 2023 and is projected to reach US$ 73.1 billion by 2033. The market is estimated to secure a CAGR of 14.8% from 2023 to 2033.

How are the Various Regions Affecting the Growth of Digital Map in the Market?

Scope of Report

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

This dataset contains additional distribution map data for various species of Cetacea and Seals included as part of the 3rd UK Habitats Directive Report submitted to the European Commission in 2013. Every six years, all EU Member States are required (under Article 17 of the Directive) to report on the implementation of the EU Habitats Directive. Further details are provided in the lineage section.

Explore a full description of the map.This map layer shows the 24 time zones commonly used in the Greenwich Mean Time model. The hours added or subtracted from the time in Greenwich are marked on the map. For example, if it is 1:00 p.m. in London, England, United Kingdom, it is 6:30 pm in New Delhi, Delhi, India (+5.50), and 5:00 a.m. in Los Angeles, California, United States (-8.00). CreditsEsri, from National Geographic MapMakerTerms of Use This work is licensed under the Esri Master License Agreement.View Summary | View Terms of Use

This map shows the extents of the various datasets comprising the World Elevation dynamic (Terrain, TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.The map has pop-ups defined. Click anywhere on the map to reveal details about the data sources.Topography sources listed in the table below are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only. Data Source Native Pixel Size Approximate Pixel Size (meters) Coverage Primary Source Country/Region

Topography

Australia 1m 1 meter 1 Partial areas of Australia Geoscience Australia Australia

Moreton Bay, Australia 1m 1 meter 1 Moreton Bay region, Australia Moreton Bay Regional Council Australia

New South Wales, Australia 5m 5 meters 5 New South Wales State, Australia DFSI Australia

SRTM 1 arc second DEM-S 0.0002777777777779 degrees 31 Australia Geoscience Australia Australia

Burgenland 50cm 0.5 meter 0.5 Burgenland State, Austria Land Burgenland Austria

Upper Austria 50cm 0.5 meter 0.5 Upper Austria State, Austria Land Oberosterreich Austria

Austria 1m 1 meter 1 Austria BEV Austria

Austria 10m 10 meters 10 Austria Geoland Austria

Canada HRDEM 1m 1 meter 1 Partial areas of the southern part of Canada Natural Resources Canada Canada

Canada HRDEM 2m 2 meters 2 Partial areas of the southern part of Canada Natural Resources Canada Canada

Denmark 40cm 0.4 meter 0.4 Denmark SDFE Denmark

Denmark 10m 10 meters 10 Denmark SDFE Denmark

England 2m 2 meters 2 70 % of England Environment Agency England

Estonia 1m 1 meter 1 Estonia Estonian Land Board Estonia

Estonia 5m 5 meters 5 Estonia Estonian Land Board Estonia

Estonia 10m 10 meters 10 Estonia Estonian Land Board Estonia

Finland 2m 2 meters 2 Finland NLS Finland

Finland 10m 10 meters 10 Finland NLS Finland

Berlin 1m 1 meter 1 Berlin State, Germany Geoportal Berlin Germany

Hamburg 1m 1 meter 1 Hamburg State, Germany LGV Hamburg Germany

Nordrhein-Westfalen 1m 1 meter 1 Nordrhein-Westfalen State, Germany Land NRW Germany

Sachsen-Anhalt 2m 2 meters 2 Sachsen-Anhalt State, Germany LVermGeo LSA Germany

Hong Kong 50cm 0.5 meter 0.5 Hong Kong CEDD Hong Kong SAR

Italy TINITALY 10m 10 meters 10 Italy INGV Italy

Japan DEM5A *, DEM5B * 0.000055555555 degrees 5 Partial areas of Japan GSI Japan

Japan DEM10B * 0.00011111111 degrees 10 Japan GSI Japan

Latvia 1m 1 meter 1 Latvia Latvian Geospatial Information Agency Latvia

Latvia 10m 10 meters 10 Latvia Latvian Geospatial Information Agency Latvia

Latvia 20m 20 meters 20 Latvia Latvian Geospatial Information Agency Latvia

Lithuania 1m 1 meter 1 Lithuania NZT Lithuania

Lithuania 10m 10 meters 10 Lithuania NZT Lithuania

Netherlands (AHN3/AHN4) 50cm 0.5 meter 0.5 Netherlands AHN Netherlands

Netherlands (AHN3/AHN4) 10m 10 meters 10 Netherlands AHN Netherlands

New Zealand 1m 1 meter 1 Partial areas of New Zealand Land Infromation New Zealand (Sourced from LINZ. CC BY 4.0) New Zealand

Northern Ireland 10m 10 meters 10 Northern Ireland OSNI Northern Ireland

Norway 10m 10 meters 10 Norway NMA Norway

Poland 1m 1 meter 1 Partial areas of Poland GUGIK Poland

Poland 5m 5 meters 5 Partial areas of Poland GUGIK Poland

Scotland 1m 1 meter 1 Partial areas of Scotland Scottish Government et.al Scotland

Slovakia 10m 10 meters 10 Slovakia GKÚ Slovakia

Slovenia 1m 1 meter 1 Slovenia ARSO Slovenia

Madrid City 1m 1 meter 1 Madrid city, Spain Ayuntamiento de Madrid Spain

Spain 2m (MDT02 2019 CC-BY 4.0 scne.es) 2 meters 2 Partial areas of Spain IGN Spain

Spain 5m 5 meters 5 Spain IGN Spain

Spain 10m 10 meters 10 Spain IGN Spain

Varnamo 50cm 0.5 meter 0.5 Varnamo municipality, Sweden Värnamo Kommun Sweden

Canton of Basel-Landschaft 25cm 0.25 meter 0.25 Canton of Basel-Landschaft, Switzerland Geoinformation Kanton Basel-Landschaft Switzerland

Grand Geneva 50cm 0.5 meter 0.5 Grand Geneva metropolitan, France/Switzerland SITG Switzerland and France

Switzerland swissALTI3D 50cm 0.5 meter 0.5 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

Switzerland swissALTI3D 10m 10 meters 10 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

OS Terrain 50 50 meters 50 United Kingdom Ordnance Survey United Kingdom

3DEP 1m 1 meter 1 Partial areas of the conterminous United States, Puerto Rico USGS United States

NRCS 1m 1 meter 1 Partial areas of the conterminous United States NRCS USDA United States

FEMA LiDAR DTM 3 meters 3 Partial areas of the conterminous United States FEMA United States

NED 1/9 arc second 0.000030864197530866 degrees 3 Partial areas of the conterminous United States USGS United States

3DEP 5m 5 meters 5 Alaska, United States USGS United States

NED 1/3 arc second 0.000092592592593 degrees 10 conterminous United States, Hawaii, Alaska, Puerto Rico, and Territorial Islands of the United States USGS United States

NED 1 arc second 0.0002777777777779 degrees 31 conterminous United States, Hawaii, Alaska, Puerto Rico, Territorial Islands of the United States; Canada and Mexico USGS United States

NED 2 arc second 0.000555555555556 degrees 62 Alaska, United States USGS United States

Wales 2m 2 meters 2 70 % of Wales Natural Resources Wales Wales

WorldDEM4Ortho 0.00022222222 degrees 24 Global (excluding the countries of Azerbaijan, DR Congo and Ukraine) Airbus Defense and Space GmbH World

SRTM 1 arc second 0.0002777777777779 degrees 31 all land areas between 60 degrees north and 56 degrees south except Australia NASA World

EarthEnv-DEM90 0.00083333333333333 degrees 93 Global N Robinson,NCEAS World

SRTM v4.1 0.00083333333333333 degrees 93 all land areas between 60 degrees north and 56 degrees south except Australia CGIAR-CSI World

GMTED2010 7.5 arc second 0.00208333333333333 degrees 232 Global USGS World

GMTED2010 15 arc second 0.00416666666666666 degrees 464 Global USGS World

GMTED2010 30 arc second 0.0083333333333333 degrees 928 Global USGS World

Bathymetry

Canada west coast 10 meters 10 Canada west coast Natural Resources Canada Canada

Gulf of Mexico 40 feet 12 Northern Gulf of Mexico BOEM Gulf of Mexico

MH370 150 meters 150 MH370 flight search area (Phase 1) of Indian Ocean Geoscience Australia Indian Ocean

Switzerland swissBATHY3D 1 - 3 meters 1, 2, 3 Lakes of Switzerland swisstopo Switzerland

NCEI 1/9 arc second 0.000030864197530866 degrees 3 Puerto Rico, U.S Virgin Islands and partial areas of eastern and western United States coast NOAA NCEI United States

NCEI 1/3 arc second 0.000092592592593 degrees 10 Partial areas of eastern and western United States coast NOAA NCEI United States

CRM 1 arc second (Version 2) 0.0002777777777779 degrees 31 Southern California coast of United States NOAA United States

NCEI 1 arc second 0.0002777777777779 degrees 31 Partial areas of northeastern United States coast NOAA NCEI United States

CRM 3 arc second 0.00083333333333333 degrees 93 United States Coast NOAA United States

NCEI 3 arc second 0.00083333333333333 degrees 93 Partial areas of northeastern United States coast NOAA NCEI United States

USGS CoNED 1 - 3 meters 1, 2, 3 Partial coastal areas of eastern and western United States USGS United States

GEBCO 2021 ** 0.00416666666666666 degrees 464 Global GEBCO World

GEBCO 2014 0.0083333333333333 degrees 928 Global GEBCO World * Fundamental Geospatial Data provided by GSI with Approval Number JYOU-SHI No.1239 2016. ** GEBCO Compilation Group (2021) GEBCO 2021 Grid (doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f) *** Bathymetry datasets are part of TopoBathy and TopoBathy3D services only.Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.

The map shows annual mean concentrations of Particulate Matter (PM2.5) in Europe based on daily averages with at least 75% of valid measurements, in µg/m3 (source: EEA, AirBase v.8 & AQ e-Reporting)Thresholds used in the maps for annual values [µg/m3]:≤ 10: (10 μg/m3, as set out in the WHO air quality guideline for PM2.5)> 10 ≤ 20: (20 μg/m3, limit value as set out in the Air Quality Directive, 2008/50/EC)> 20 ≤ 25: (25 μg/m3, target value as set out in the Air Quality Directive, 2008/50/EC)> 25 ≤ 30> 30Source: AirBase v.8 & AQ e-ReportingAirBase is the European air quality database maintained by the EEA through its European topic centre on Air pollution and Climate Change mitigation. It contains air quality monitoring data and information submitted by participating countries throughout Europe.The air quality database consists of a multi-annual time series of air quality measurement data and statistics for a number of air pollutants. It also contains meta-information on those monitoring networks involved, their stations and their measurements.The database covers geographically all EU Member States, the EEA member countries and some EEA collaborating countries. The EU Member States are bound under Decision 97/101/EC to engage in a reciprocal exchange of information (EoI) on ambient air quality. The EEA engages with its member and collaborating countries to collect the information foreseen by the EoI Decision because air pollution is a pan European issue and the EEA is the European body which produces assessments of air quality, covering the whole geographical area of Europe.

PLEASE NOTE: This data product is not available in Shapefile format or KML at https://naturalengland-defra.opendata.arcgis.com/datasets/Defra::living-england-habitat-map-phase-4/about, as the data exceeds the limits of these formats. Please select an alternative download format.This data product is also available for download in multiple formats via the Defra Data Services Platform at https://environment.data.gov.uk/explore/4aa716ce-f6af-454c-8ba2-833ebc1bde96?download=true.The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable. Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes. Datasets used:Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate DataFull metadata can be viewed on data.gov.uk.