The State of Michigan (DTMB) contracted with Sanborn to provide LiDAR mapping services for 6 counties in the State of Michigan. These counties include Cass, Genesee, Kalamazoo, Lapeer, Shiawassee, and St, Joseph. This metadata record describes the three counties of Cass, Kalamazoo, and St. Joseph and the data entry in the NOAA Digital Coast Data Access Viewer (DAV). For this data set, the DAV i...

Global Mobile Mapping Market size was valued at USD 24.18 billion in 2022 and is poised to grow from USD 28.1 billion in 2023 to USD 93.39 billion by 2031, growing at a CAGR of 16.2% in the forecast period (2024-2031).

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface.

Produced by the Environment Agency in 2022, the DTM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.

The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.

The data is available to download as GeoTiff rasters in 5km tiles aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE.

High-Definition Map Market Outlook

According to our latest research, the global High-Definition (HD) Map market size reached USD 3.1 billion in 2024. The market is experiencing robust expansion, propelled by the rapid advancements in autonomous driving technologies and the proliferation of connected vehicles. The industry is forecasted to grow at a CAGR of 17.6% from 2025 to 2033, reaching an estimated USD 15.4 billion by 2033. This remarkable growth trajectory is underpinned by the surging demand for real-time, highly accurate mapping solutions that are essential for the safe and efficient operation of autonomous vehicles and advanced driver assistance systems (ADAS).

One of the primary growth drivers for the High-Definition Map market is the accelerating adoption of autonomous vehicles across major automotive markets. HD maps provide centimeter-level accuracy, lane-level guidance, and rich contextual information, which are crucial for the navigation and decision-making processes of self-driving cars. As automotive OEMs and technology companies intensify their investments in autonomous mobility, the need for continuously updated and precise mapping data is becoming indispensable. The integration of HD maps with sensor fusion technologies, such as LiDAR, radar, and computer vision, further enhances vehicle perception, thereby boosting safety and reliability in complex driving environments.

Another significant factor fueling market growth is the increasing implementation of advanced driver assistance systems (ADAS) in modern vehicles. Regulatory mandates for safety features like adaptive cruise control, lane-keeping assistance, and automated emergency braking are driving OEMs to incorporate HD mapping solutions as a foundational layer for these applications. The synergy between real-time sensor data and HD map information enables vehicles to anticipate road conditions, recognize traffic signs, and navigate complex intersections with greater accuracy. This trend is not limited to passenger vehicles; commercial fleets and logistics operators are also leveraging HD maps to optimize route planning, minimize fuel consumption, and enhance overall operational efficiency.

The proliferation of connected infrastructure and smart city initiatives is also catalyzing the growth of the High-Definition Map market. Governments and municipalities are partnering with mapping solution providers to develop digital twins of urban environments, facilitating intelligent transportation systems and dynamic traffic management. These initiatives are creating new opportunities for HD map vendors to offer cloud-based, real-time mapping services that support a wide range of applications, from public transit optimization to emergency response coordination. The convergence of IoT, 5G connectivity, and geospatial analytics is expected to further expand the scope and scale of HD map deployments in the coming years.

Regionally, North America and Europe remain at the forefront of HD map adoption, owing to their advanced automotive ecosystems and supportive regulatory frameworks. Asia Pacific is emerging as a high-growth region, driven by rapid urbanization, rising vehicle ownership, and significant investments in smart mobility infrastructure. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with increasing interest from government agencies and logistics providers in leveraging HD maps for transportation modernization. The global landscape is characterized by a dynamic interplay of technology innovation, regulatory evolution, and cross-industry collaboration, setting the stage for sustained market expansion through 2033.

Solution Analysis

The High-Definition Map market is segmented by solution into Cloud-Based and Embedded offerings, each catering to distinct use cases and customer requirements. Cloud-based HD mapping solutions have gained significant traction in recent years, primarily due

Camp Dresser McKee Inc. contracted with Sanborn Map Company to provide LiDAR mapping services for Fort Kent, Maine. Utilizing multi-return systems, Light Detection and Ranging (LiDAR) data in the form of 3-dimensional positions of a dense set of mass points was collected in spring 2009 for 187 square miles along the St. Johns River and the Fish River. The Leica ALS-50 LiDAR system was used to collect data for the survey campaign. The nominal point spacing of this data set is 1.4 meters. Leica ALS-50 LiDAR System Acquisition Parameters: Average Altitude: 1400 Meters above ground level Airspeed: ~120 Knots Scan Frequency: 36 Hertz Scan Width Half Angle: 20 Degrees Pulse Rate: 76,200 Hertz

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

Location and HD Map Unit Market Outlook

The global market size for Location and HD Map Unit was valued at $8.3 billion in 2023 and is projected to reach $26.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.8% from 2024 to 2032. This robust growth is primarily driven by advancements in autonomous driving technologies, increasing integration of Artificial Intelligence (AI) in navigation systems, and the rising demand for high-precision mapping solutions across various industries.

One of the primary growth factors for the Location and HD Map Unit market is the surge in demand for autonomous vehicles. Automotive manufacturers and technology providers are heavily investing in developing self-driving cars, which rely on accurate and real-time location data as well as HD maps. These maps are essential for the proper functioning of advanced driver assistance systems (ADAS) that ensure the safety and efficiency of autonomous driving. The growing emphasis on reducing traffic accidents and enhancing road safety is also a key driver for market growth.

Another significant factor propelling market expansion is the rise of smart cities. Governments and urban planners are increasingly focusing on integrating cutting-edge technologies to improve urban infrastructure and transportation. Location and HD map units play a crucial role in the development of smart city initiatives by providing precise geospatial data that helps in efficient traffic management, urban planning, and the deployment of public services. The increasing adoption of Internet of Things (IoT) technologies further amplifies the need for high-accuracy mapping solutions.

Moreover, the logistics and fleet management sectors are witnessing substantial growth due to the adoption of Location and HD map units. With the advent of e-commerce and the need for efficient delivery systems, logistics companies are leveraging high-definition maps to optimize their routes, reduce delivery times, and enhance customer satisfaction. Fleet management systems also benefit from real-time mapping solutions for effective vehicle tracking, fuel management, and predictive maintenance, thereby driving the market forward.

High Accuracy Map technology is becoming increasingly vital in the development of autonomous vehicles and smart city infrastructures. These maps provide precise geospatial data that is crucial for navigation and real-time decision-making. By offering detailed information about road conditions, traffic patterns, and environmental factors, High Accuracy Maps enable vehicles to operate safely and efficiently in complex urban environments. The integration of AI and machine learning further enhances the capabilities of these maps, allowing for continuous updates and improvements in accuracy. As the demand for autonomous driving and smart city solutions grows, the importance of High Accuracy Maps in ensuring seamless and safe navigation cannot be overstated.

From a regional perspective, North America holds the dominant share in the Location and HD Map Unit market, thanks to the presence of major technology companies and automotive giants. The region's well-established infrastructure and early adoption of advanced technologies contribute to its leading position. However, Asia Pacific is expected to witness the highest growth rate during the forecast period owing to rapid urbanization, increasing investments in smart cities, and the burgeoning automotive sector, particularly in countries like China, Japan, and South Korea.

Component Analysis

The Location and HD Map Unit market is segmented by component into hardware, software, and services. The hardware segment includes sensors, cameras, and LiDAR systems, which are crucial for capturing real-time data for mapping. The software segment encompasses the algorithms and platforms used for processing and analyzing the collected data to create high-definition maps. Services include installation, maintenance, and consulting services offered by solution providers.

In the hardware segment, sensors and LiDAR systems are witnessing significant demand due to their ability to capture high-resolution spatial data. These components are essential for the functioning of autonomous vehicles and robotics. The growing adoption of LiDAR technology in various applications such as automotive, drones, and smart cities is contributing to the expansion of the hardware segment. Additionally, advancements in sensor tec

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The Automotive 3D Map System market is experiencing robust growth, driven by the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies. The market size in 2025 is estimated at $10 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors: the escalating demand for enhanced navigation and location-based services, the proliferation of connected cars, and the imperative for improved safety and efficiency in autonomous vehicles. The integration of high-definition 3D maps into vehicle systems allows for precise localization, improved route planning, and the enablement of crucial safety features like lane keeping assist and automatic emergency braking. Furthermore, the continuous advancements in sensor technologies, such as LiDAR and radar, are contributing to the increased accuracy and detail of 3D maps, further accelerating market growth. Significant investments from both established automotive players and technology companies are fueling innovation and competition within the sector. Market segmentation reveals a dynamic landscape, with in-dash navigation systems holding a significant share currently, followed by portable navigation devices. However, the application in passenger vehicles dominates the market, albeit with substantial growth anticipated in light commercial vehicles, heavy-duty trucks, buses, and off-road vehicles. Geographic distribution shows strong market penetration in North America and Europe, driven by early adoption of autonomous vehicle technologies and stringent safety regulations. However, the Asia-Pacific region is projected to experience the fastest growth, fueled by increasing vehicle production and rising disposable incomes in developing economies. Restraints to growth include high initial investment costs for 3D mapping infrastructure and the need for robust cybersecurity measures to safeguard sensitive location data. Despite these challenges, the long-term outlook remains positive, with continued technological advancements and increasing government support paving the way for sustained expansion of the automotive 3D map system market.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the NOAA Lake Level Viewer. It depicts potential lake level rise and fall and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at lake level change, coastal flooding impacts, and exposed lakeshore. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The NOAA Lake Level Viewer may be accessed at: https://coast.noaa.gov/llv. This metadata record describes the Lake Superior digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Lake Level Viewer described above. This DEM includes the best available lidar, US Army Corps of Engineer dredge surveys, and National Park Service multibeam data known to exist at the time of DEM creation that met project specifications. This DEM includes data for Alger, Baraga, Chippewa, Gogebic, Houghton, Keweenaw, Luce, Marquette, and Ontonagon counties in Michigan; Cook, Lake, and St. Louis counties in Minnesota; and Ashland, Bayfield, Douglas, and Iron counties in Wisconsin. The DEM was produced from the following lidar data sets: 1. 2007, USACE NCMP Topobathy Lidar: Lake Superior (Apostle Islands) and Lake Ontario (NY, WI) 2. 2008, USACE NCMP Topobathy Lidar: Lake Superior (Wisconsin and Michigan) 3. 2009, USACE NCMP Topobathy Lidar: Lake Superior (Duluth, MN) 4. 2009, USACE NCMP Topobathy Lidar: Isle Royale (MI) 5. 2009, USACE NCMP Topobathy Lidar: Apostle Islands, Wisconsin 6. 2009, USACE Lidar: Duluth, MN and Superior, WI (Including shoreline in Douglas, Bayfield, Ashland, and Iron Counties) 7. 2010, EPA Great Lakes Restoration Initiative (GLRI) Bathymetric Lidar: Lake Superior (MI, MN, WI) 8. 2011, USACE NCMP Topobathy Lidar: MI/NY Great Lakes 9. 2011, Northeast Minnesota / Arrowhead Lidar 10. 2013, USACE NCMP Topobathy Lidar: Stamp Sands, Lake Superior (MI) 11. 2013, USACE NCMP Topobathy Lidar: St. Marys River (MI) 12. 2013, USACE NCMP Topobathy Lidar: Lake Superior (MI) 13. 2015, FEMA Ashland County 14. 2016, USACE NCMP Topobathy Lidar: Stamp Sands (MI) The DEM was produced from the following sonar data sets: 15. USACE Harbor Dredge Surveys (9 surveys) 16. 2013, National Park Service, Pictured Rocks National Lakeshore Multibeam Sonar 17. 2014, National Park Service, Pictured Rocks National Lakeshore Multibeam Sonar The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

The global 3D mapping and modeling market is expected to grow significantly in the next few years as demand increases for detailed and accurate representations of physical environments in three-dimensional space. Estimated to be valued at USD 38.62 billion in the year 2025, the market was expected to grow at a CAGR of 14.5% from 2025 to 2033 and was estimated to reach an amount of USD 90.26 billion by the end of 2033. The high growth rate is because of improvement in advanced technologies with the development of high-resolution sensors and methods of photogrammetry that make possible higher-resolution realistic and immersive 3D models.Key trends in the market are the adoption of virtual and augmented reality (VR/AR) applications, 3D mapping with smart city infrastructure, and increased architecture, engineering, and construction utilization of 3D models. Other factors are driving the growing adoption of cloud-based 3D mapping and modeling solutions. The solutions promise scalability, cost-effectiveness, and easy access to 3D data, thus appealing to business and organizations of all sizes. Recent developments include: Jun 2023: Nomoko (Switzerland), a leading provider of real-world 3D data technology, announced that it has joined the Overture Maps Foundation, a non-profit organization committed to fostering collaboration and innovation in the geospatial domain. Nomoko will collaborate with Meta, Amazon Web Services (AWS), TomTom, and Microsoft, to create interoperable, accessible 3D datasets, leveraging its real-world 3D modeling capabilities., May 2023: The Sanborn Map Company (Sanborn), an authority in 3D models, announced the development of a powerful new tool, the Digital Twin Base Map. This innovative technology sets a new standard for urban analysis, implementation of Digital Cities, navigation, and planning with a fundamental transformation from a 2D map to a 3D environment. The Digital Twin Base Map is a high-resolution 3D map providing unprecedented detail and accuracy., Feb 2023: Bluesky Geospatial launched the MetroVista, a 3D aerial mapping program in the USA. The service employs a hybrid imaging-Lidar airborne sensor to capture highly detailed 3D data, including 360-degree views of buildings and street-level features, in urban areas to create digital twins, visualizations, and simulations., Feb 2023: Esri, a leading global provider of geographic information system (GIS), location intelligence, and mapping solutions, released new ArcGIS Reality Software to capture the world in 3D. ArcGIS Reality enables site, city, and country-wide 3D mapping for digital twins. These 3D models and high-resolution maps allow organizations to analyze and interact with a digital world, accurately showing their locations and situations., Jan 2023: Strava, a subscription-based fitness platform, announced the acquisition of FATMAP, a 3D mapping platform, to integrate into its app. The acquisition adds FATMAP's mountain-focused maps to Strava's platform, combining with the data already within Strava's products, including city and suburban areas for runners and other fitness enthusiasts., Jan 2023: The 3D mapping platform FATMAP is acquired by Strava. FATMAP applies the concept of 3D visualization specifically for people who like mountain sports like skiing and hiking., Jan 2022: GeoScience Limited (the UK) announced receiving funding from Deep Digital Cornwall (DDC) to develop a new digital heat flow map. The DDC project has received grant funding from the European Regional Development Fund. This study aims to model the heat flow in the region's shallower geothermal resources to promote its utilization in low-carbon heating. GeoScience Ltd wants to create a more robust 3D model of the Cornwall subsurface temperature through additional boreholes and more sophisticated modeling techniques., Aug 2022: In order to create and explore the system's possibilities, CGTrader worked with the online retailer of dietary supplements Hello100. The system has the ability to scale up the generation of more models, and it has enhanced and improved Hello100's appearance on Amazon Marketplace.. Key drivers for this market are: The demand for 3D maps and models is growing rapidly across various industries, including architecture, engineering, and construction (AEC), manufacturing, transportation, and healthcare. Advances in hardware, software, and data acquisition techniques are making it possible to create more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations.

. Potential restraints include: The acquisition and processing of 3D data can be expensive, especially for large-scale projects. There is a lack of standardization in the 3D mapping modeling industry, which can make it difficult to share and exchange data between different software and systems. There is a shortage of skilled professionals who are able to create and use 3D maps and models effectively.. Notable trends are: 3D mapping and modeling technologies are becoming essential for a wide range of applications, including urban planning, architecture, construction, environmental management, and gaming. Advancements in hardware, software, and data acquisition techniques are enabling the creation of more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations..

A first surface elevation map (also known as a Digital Elevation Model, or DEM) of a portion of St. John, U.S. Virgin Islands was produced from remotely sensed, geographically referenced elevation measurements cooperatively by the U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and National Park Service (NPS). Elevation measurements were collected over the area using the NASA Experimental Advanced Airborne Research Lidar (EAARL), a pulsed-laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters. The EAARL, developed by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 15 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When subsequent elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

For more information on Lidar science and the Experimental Advanced Airborne Research Lidar (EAARL) system and surveys, see http://ngom.usgs.gov/dsp/overview/index.php and http://ngom.usgs.gov/dsp/tech/eaarl/index.php .

The lidar survey was conducted by vendor Spectrum Mapping LCC, a Lidar company. Lidar instrument Datis II sensor was flown over the period of 10 and 12 July 2004. The data were delivered in ASCII format (files separated by vegetation points and ground points) with information on X, Y, elevation, Return number, RGB and scan angle. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The project area consists of one contiguous blocks totaling 13234 hectares in Emerald Creek, St. Joe National Forest, Idaho. The project area consists of moderately variable topographic configurations with diverse vegetation components.

High Precision Real Time Map Market Outlook

In 2023, the global high precision real time map market size was valued at approximately USD 4.8 billion. Forecasts indicate significant growth, with the market size anticipated to reach USD 19.6 billion by 2032, reflecting a compound annual growth rate (CAGR) of 16.8%. This impressive growth is driven by advancements in autonomous vehicle technology, increasing demand for smart city solutions, and the expansion of high-speed connectivity infrastructure.

The rapid adoption of autonomous and semi-autonomous vehicles is a primary growth factor for the high precision real time map market. These vehicles require detailed and continuously updated maps to navigate safely and efficiently. High precision maps provide essential data, such as lane markings, traffic signals, and road signs, enabling vehicles to make real-time driving decisions. The technological advancements in sensors, such as LiDAR and radar, further enhance the accuracy and reliability of these maps, fueling market growth.

Another significant growth factor is the increasing demand for smart city solutions. Urban areas are becoming more interconnected, and real time maps play a crucial role in managing traffic flow, optimizing public transportation routes, and enhancing emergency response times. The integration of Internet of Things (IoT) devices and 5G connectivity is transforming urban planning and management, creating a fertile ground for the expansion of high precision real time mapping solutions. Governments and municipalities are investing heavily in smart city infrastructure, further propelling the market forward.

The logistics and supply chain industry also significantly contributes to the growth of the high precision real time map market. Accurate and real-time mapping solutions improve route optimization, reduce delivery times, and enhance overall operational efficiency. With the rise of e-commerce and the increasing expectations for faster delivery times, logistics companies are leveraging high precision maps to gain a competitive edge. The use of these maps in drone deliveries and autonomous delivery vehicles is also emerging as a key trend in the industry.

Regionally, North America dominates the high precision real time map market, driven by the early adoption of advanced technologies and substantial investments in autonomous vehicle research and development. The presence of leading technology companies and automotive manufacturers in the region further fuels market growth. However, the Asia Pacific region is expected to witness the highest growth during the forecast period. Rapid urbanization, increasing investments in smart city projects, and the growing automotive industry in countries like China and India are key factors contributing to the market expansion in this region.

The emergence of HD Maps is revolutionizing the landscape of high precision real time mapping. These maps provide a level of detail and accuracy that is indispensable for the safe operation of autonomous vehicles. Unlike traditional maps, HD Maps include comprehensive data layers such as precise lane geometry, road slopes, and even the height of curbs. This granularity allows autonomous systems to anticipate and respond to road conditions with remarkable precision. The integration of HD Maps with real-time sensor data enables vehicles to navigate complex environments seamlessly, enhancing both safety and efficiency. As the demand for autonomous driving solutions grows, the development and deployment of HD Maps are becoming increasingly critical, driving further innovation in the mapping industry.

Component Analysis

The high precision real time map market is segmented by component into hardware, software, and services. The hardware segment includes sensors like LiDAR, radar, and GPS devices that collect and transmit data for mapping purposes. The advancements in sensor technology have significantly improved the accuracy and reliability of real-time maps. LiDAR technology, for instance, offers high-resolution 3D mapping capabilities, essential for autonomous vehicle navigation and urban planning applications.

The software segment encompasses mapping platforms and applications that process and analyze data collected by hardware components. These software solutions are crucial for creating, updating, and managing high precision maps. The integration of artificial intelligence and machine learning algorithms in mapping software e

A submerged topography elevation map (also known as a Digital Elevation Model, or DEM) of a portion of the U.S. Virgin Islands was produced from remotely sensed, geographically referenced elevation measurements cooperatively by the U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and National Park Service (NPS). Elevation measurements were collected over the area using the NASA Experimental Advanced Airborne Research Lidar (EAARL), a pulsed-laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters. The EAARL, developed by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 15 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be easily surveyed within a 3- to 4-hour mission time period. When subsequent elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

For more information on Lidar science and the Experimental Advanced Airborne Research Lidar (EAARL) system and surveys, see http://ngom.usgs.gov/dsp/overview/index.php and http://ngom.usgs.gov/dsp/tech/eaarl/index.php .

THIS MAP IS NOT AUTHORITATIVE. SEE TERMS OF USE BELOW.This web map was developed by the National Oceanic and Atmospheric Administration’s (NOAA) Office for Coastal Management and is featured in the U.S. Great Lakes Collaborative Benthic Habitat Mapping Common Operating Dashboard in support of the Collaborative Benthic Habitat Mapping in the Nearshore Waters of the Great Lakes Basin Project. This multi-year, multi-agency project is funded through the Great Lakes Restoration Initiative (GLRI) and focuses on new bathymetric data (airborne lidar and vessel based sonar) acquisition, validation, and benthic habitat characterization mapping of the nearshore waters (0-80 meters) in the U.S. Great Lakes. This project also contributes to the regional Lakebed 2030 campaign, which aims to have high-density bathymetric data available for the entirety of the Great Lakes by 2030. This web map contains data layers reflecting the current status of bathy data coverage in the nearshore (0-80 meters) of the U.S. Great Lakes, including acquisition (lidar and multibeam sonar), ground-truthing/validation, and benthic habitat mapping and characterization. Acquisition layers include coverage areas that have been acquired and are available for public use (green) as well as those that have been acquired, but are not yet available or are still in progress (orange). The nearshore water depth layers (0-25 and 25-80 meters) were created using the National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts. The 0 to 25 meter nearshore water depth layer represents areas where bathymetric lidar data acquisition could ideally be conducted, depending on water condition and turbidity. The 25 to 80 meter layer shows locations where acoustic data acquisition can occur. See below for information on additional data layers. All data originally projected in the following coordinate system: EPSG:3175, NAD 1983 Great Lakes and St Lawrence Albers.This map will continue to be updated as new information is made available.Source Data for Bathy Coverage Layers - Acquired/Available:Topobathy and Bathy Lidar (NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/). Multibeam Sonar (National Centers for Environmental Information (NCEI) Bathymetric Data Viewer: https://www.ncei.noaa.gov/maps/bathymetry/; NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/; USGS ScienceBaseCatalog: https://www.sciencebase.gov/catalog/item/656e229bd34e7ca10833f950)Source Data for Bathy Coverage Layers - GLRI AOIs (2020-2024):Acquisition: NOAA Office for Coastal ManagementValidation/CMECS Characterizations: NOAA National Centers for Coastal Ocean Science (NCCOS)Source Data for Bathy Coverage Layers - In Progress and Planned:NOAA Office of Coast Survey Plans: https://gis.charttools.noaa.gov/arcgis/rest/services/Hydrographic_Services/Planned_Survey_Areas/MapServer/0NOAA Office for Coastal ManagementSource Data for Nearshore Water Depths:NOAA's National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts: https://www.ncei.noaa.gov/maps/grid-extract/Source Data for Spatial Prioritization Layers:Great Lakes Spatial Priorities Study Results Jun 2021. https://gis.charttools.noaa.gov/arcgis/rest/services/IOCM/GreatLakes_SPS_Results_Jun_2021/MapServerMapping priorities within the proposed Wisconsin Lake Michigan National Marine Sanctuary (2018). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_WILMPrioritizationResults/MapServerThunder Bay National Marine Sanctuary Spatial Prioritization Results (2020). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_TBNMSPrioritizationResults/MapServerSource Data for Supplemental Data Layers:International Boundary Commission U.S./Canada Boundary (version 1.3 from 2018): https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.phpNational Oceanic and Atmospheric Administration (NOAA) HydroHealth 2018 Survey: https://wrecks.nauticalcharts.noaa.gov/arcgis/rest/services/Hydrographic_Services/HydroHealth_2018/ImageServerNational Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Inventory 2023-2024: https://www.fisheries.noaa.gov/inport/item/69506National Oceanic and Atmospheric Administration (NOAA) National Marine Sanctuary Program Boundaries (2021): https://services2.arcgis.com/C8EMgrsFcRFL6LrL/arcgis/rest/services/ONMS_2021_Boundaries/FeatureServerNational Oceanic and Atmospheric Administration (NOAA) U.S. Bathymetry Gap Analysis: https://noaa.maps.arcgis.com/home/item.html?id=4d7d925fc96d47d9ace970dd5040df0aU.S. Environment Protection Agency (EPA) Areas of Concern: https://services.arcgis.com/cJ9YHowT8TU7DUyn/arcgis/rest/services/epa_areas_of_concern_glahf_viewlayer/FeatureServerU.S. Geological Survey (USGS) Great Lakes Subbasins: https://www.sciencebase.gov/catalog/item/530f8a0ee4b0e7e46bd300dd Latest update: February 20, 2025

The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the one stop source for environmental and natural resource data, available anytime, from anywhere. It allows a user to choose an area of interest, browse and select data, customize the format, then download or have it shipped on media. The map layers include data on: Public Land Survey System (PLSS), Census data, demographic statistics, precipitation, temperature, disaster events, conservation easements, elevation, geographic names, geology, government units, hydrography, hydrologic units, land use and land cover, map indexes, ortho imagery, soils, topographic images, and streets and roads. This service is made available through a close partnership between the three Service Center Agencies (SCA): Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA), and Rural Development (RD). Resources in this dataset:Resource Title: Geospatial Data Gateway. File Name: Web Page, url: https://gdg.sc.egov.usda.gov This is the main page for the GDG that includes several links to view, download, or order various datasets. Find additional status maps that indicate the location of data available for each map layer in the Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGHome_StatusMaps.aspx

THIS MAP IS NOT AUTHORITATIVE. SEE TERMS OF USE BELOW.This web map was developed by the National Oceanic and Atmospheric Administration’s (NOAA) Office for Coastal Management and is featured in the U.S. Great Lakes Collaborative Benthic Habitat Mapping Common Operating Dashboard in support of the Collaborative Benthic Habitat Mapping in the Nearshore Waters of the Great Lakes Basin Project. This multi-year, multi-agency project is funded through the Great Lakes Restoration Initiative (GLRI) and focuses on new bathymetric data (airborne lidar and vessel based sonar) acquisition, validation, and benthic habitat characterization mapping of the nearshore waters (0-80 meters) in the U.S. Great Lakes. This project also contributes to the regional Lakebed 2030 campaign, which aims to have high-density bathymetric data available for the entirety of the Great Lakes by 2030. This web map contains data layers reflecting the current status of bathy data coverage in the nearshore (0-80 meters) of the U.S. Great Lakes, including acquisition (lidar and multibeam sonar), ground-truthing/validation, and benthic habitat mapping and characterization. Acquisition layers include coverage areas that have been acquired and are available for public use (green) as well as those that have been acquired, but are not yet available or are still in progress (orange). The nearshore water depth layers (0-25 and 25-80 meters) were created using the National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts. The 0 to 25 meter nearshore water depth layer represents areas where bathymetric lidar data acquisition could ideally be conducted, depending on water condition and turbidity. The 25 to 80 meter layer shows locations where acoustic data acquisition can occur. The acquired data values are all in sq. km and were created by merging and dissolving all publicly available bathy lidar and multibeam sonar coverage polygons into single layer and erasing from the nearshore water depth layers (0-25, 25-80, and 0-80 meters). All polygon layers were clipped using the USGS Great Lakes subbasin polygon shapefile and the U.S./Canada boundary from the International Boundary Commission (version 1.3 from 2018). All data originally projected in the following coordinate system: EPSG:3175, NAD 1983 Great Lakes and St Lawrence Albers.This map will continue to be updated as new information is made available.See below for information on additional data layers. Source Data for Bathy Coverage Layers - Acquired/Available:Topobathy and Bathy Lidar (NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/). Multibeam Sonar (National Centers for Environmental Information (NCEI) Bathymetric Data Viewer: https://www.ncei.noaa.gov/maps/bathymetry/; NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/; USGS ScienceBaseCatalog: https://www.sciencebase.gov/catalog/item/656e229bd34e7ca10833f950)Source Data for Bathy Coverage Layers - GLRI AOIs (2020-2024):Acquisition: NOAA Office for Coastal ManagementValidation/CMECS Characterizations: NOAA National Centers for Coastal Ocean Science (NCCOS)Source Data for Bathy Coverage Layers - In Progress and Planned:NOAA Office of Coast Survey Plans: https://gis.charttools.noaa.gov/arcgis/rest/services/Hydrographic_Services/Planned_Survey_Areas/MapServer/0NOAA Office for Coastal ManagementSource Data for Nearshore Water Depths:NOAA's National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts: https://www.ncei.noaa.gov/maps/grid-extract/Source Data for Spatial Prioritization Layers:Great Lakes Spatial Priorities Study Results Jun 2021. https://gis.charttools.noaa.gov/arcgis/rest/services/IOCM/GreatLakes_SPS_Results_Jun_2021/MapServerMapping priorities within the proposed Wisconsin Lake Michigan National Marine Sanctuary (2018). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_WILMPrioritizationResults/MapServerThunder Bay National Marine Sanctuary Spatial Prioritization Results (2020). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_TBNMSPrioritizationResults/MapServerSource Data for Supplemental Data Layers:International Boundary Commission U.S./Canada Boundary (version 1.3 from 2018): https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.phpNational Oceanic and Atmospheric Administration (NOAA) HydroHealth 2018 Survey: https://wrecks.nauticalcharts.noaa.gov/arcgis/rest/services/Hydrographic_Services/HydroHealth_2018/ImageServerNational Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Inventory 2023-2024: https://www.fisheries.noaa.gov/inport/item/69506National Oceanic and Atmospheric Administration (NOAA) National Marine Sanctuary Program Boundaries (2021): https://services2.arcgis.com/C8EMgrsFcRFL6LrL/arcgis/rest/services/ONMS_2021_Boundaries/FeatureServerNational Oceanic and Atmospheric Administration (NOAA) U.S. Bathymetry Gap Analysis: https://noaa.maps.arcgis.com/home/item.html?id=4d7d925fc96d47d9ace970dd5040df0aU.S. Environment Protection Agency (EPA) Areas of Concern: https://services.arcgis.com/cJ9YHowT8TU7DUyn/arcgis/rest/services/epa_areas_of_concern_glahf_viewlayer/FeatureServerU.S. Geological Survey (USGS) Great Lakes Subbasins: https://www.sciencebase.gov/catalog/item/530f8a0ee4b0e7e46bd300dd Latest update: February 20, 2025

The i.c.sens Visual-Inertial-LiDAR Dataset is a data set for the evaluation of dead reckoning or SLAM approaches in the context of mobile robotics. It consists of street-level monocular RGB camera images, a front-facing 180° point cloud, angular velocities, accelerations and an accurate ground truth trajectory. In total, we provide around 77 GB of data resulting from a 15 minutes drive, which is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the intrinsic camera parameters and the extrinsic transformations between all sensor coordinate systems are given. Details on the data and its usage can be found in the provided documentation file. Image credit: Sören Vogel The data set was acquired in the context of the measurement campaign described in Schoen2018. Here, a vehicle, which can be seen below, was equipped with a self-developed sensor platform and a commercially available Riegl VMX-250 Mobile Mapping System. This Mobile Mapping System consists of two laser scanners, a camera system and a localization unit containing a highly accurate GNSS/IMU system. Image credit: Sören Vogel The data acquisition took place in May 2019 during a sunny day in the Nordstadt of Hannover (coordinates: 52.388598, 9.716389). The route we took can be seen below. This route was completed three times in total, which amounts to a total driving time of 15 minutes. The self-developed sensor platform consists of several sensors. This dataset provides data from the following sensors: Velodyne HDL-64 LiDAR LORD MicroStrain 3DM-GQ4-45 GNSS aided IMU Pointgrey GS3-U3-23S6C-C RGB camera To inspect the data, first start a rosmaster and launch rviz using the provided configuration file: roscore & rosrun rviz rviz -d icsens_data.rviz

THIS MAP IS NOT AUTHORITATIVE. SEE TERMS OF USE BELOW.This web map was developed by the National Oceanic and Atmospheric Administration’s (NOAA) Office for Coastal Management and is featured in the U.S. Great Lakes Collaborative Benthic Habitat Mapping Common Operating Dashboard in support of the Collaborative Benthic Habitat Mapping in the Nearshore Waters of the Great Lakes Basin Project. This multi-year, multi-agency project is funded through the Great Lakes Restoration Initiative (GLRI) and focuses on new bathymetric data (airborne lidar and vessel based sonar) acquisition, validation, and benthic habitat characterization mapping of the nearshore waters (0-80 meters) in the U.S. Great Lakes. This project also contributes to the regional Lakebed 2030 campaign, which aims to have high-density bathymetric data available for the entirety of the Great Lakes by 2030. This web map contains data layers reflecting the current status of bathy data coverage in the nearshore (0-80 meters) of the U.S. Great Lakes, including acquisition (lidar and multibeam sonar), ground-truthing/validation, and benthic habitat mapping and characterization. Acquisition layers include coverage areas that have been acquired and are available for public use (green) as well as those that have been acquired, but are not yet available or are still in progress (orange). The nearshore water depth layers (0-25 and 25-80 meters) were created using the National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts. The 0 to 25 meter nearshore water depth layer represents areas where bathymetric lidar data acquisition could ideally be conducted, depending on water condition and turbidity. The 25 to 80 meter layer shows locations where acoustic data acquisition can occur. See below for information on additional data layers. All data originally projected in the following coordinate system: EPSG:3175, NAD 1983 Great Lakes and St Lawrence Albers.This map will continue to be updated as new information is made available.Source Data for Bathy Coverage Layers - Acquired/Available:Topobathy and Bathy Lidar (NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/). Multibeam Sonar (National Centers for Environmental Information (NCEI) Bathymetric Data Viewer: https://www.ncei.noaa.gov/maps/bathymetry/; NOAA's Data Access Viewer: https://coast.noaa.gov/dataviewer/#/; U.S. Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/; USGS ScienceBaseCatalog: https://www.sciencebase.gov/catalog/item/656e229bd34e7ca10833f950)Source Data for Bathy Coverage Layers - GLRI AOIs (2020-2024):Acquisition: NOAA Office for Coastal ManagementValidation/CMECS Characterizations: NOAA National Centers for Coastal Ocean Science (NCCOS)Source Data for Bathy Coverage Layers - In Progress and Planned:NOAA Office of Coast Survey Plans: https://gis.charttools.noaa.gov/arcgis/rest/services/Hydrographic_Services/Planned_Survey_Areas/MapServer/0NOAA Office for Coastal ManagementSource Data for Nearshore Water Depths:NOAA's National Centers for Environmental Information (NCEI) Great Lakes Bathymetry (3-second resolution) grid extracts: https://www.ncei.noaa.gov/maps/grid-extract/Source Data for Spatial Prioritization Layers:Great Lakes Spatial Priorities Study Results Jun 2021. https://gis.charttools.noaa.gov/arcgis/rest/services/IOCM/GreatLakes_SPS_Results_Jun_2021/MapServerMapping priorities within the proposed Wisconsin Lake Michigan National Marine Sanctuary (2018). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_WILMPrioritizationResults/MapServerThunder Bay National Marine Sanctuary Spatial Prioritization Results (2020). https://gis.ngdc.noaa.gov/arcgis/rest/services/nccos/BiogeographicAssessments_TBNMSPrioritizationResults/MapServerSource Data for Supplemental Data Layers:International Boundary Commission U.S./Canada Boundary (version 1.3 from 2018): https://www.internationalboundarycommission.org/en/maps-coordinates/coordinates.phpNational Oceanic and Atmospheric Administration (NOAA) HydroHealth 2018 Survey: https://wrecks.nauticalcharts.noaa.gov/arcgis/rest/services/Hydrographic_Services/HydroHealth_2018/ImageServerNational Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Inventory 2023-2024: https://www.fisheries.noaa.gov/inport/item/69506National Oceanic and Atmospheric Administration (NOAA) National Marine Sanctuary Program Boundaries (2021): https://services2.arcgis.com/C8EMgrsFcRFL6LrL/arcgis/rest/services/ONMS_2021_Boundaries/FeatureServerNational Oceanic and Atmospheric Administration (NOAA) U.S. Bathymetry Gap Analysis: https://noaa.maps.arcgis.com/home/item.html?id=4d7d925fc96d47d9ace970dd5040df0aU.S. Environment Protection Agency (EPA) Areas of Concern: https://services.arcgis.com/cJ9YHowT8TU7DUyn/arcgis/rest/services/epa_areas_of_concern_glahf_viewlayer/FeatureServerU.S. Geological Survey (USGS) Great Lakes Subbasins: https://www.sciencebase.gov/catalog/item/530f8a0ee4b0e7e46bd300dd Latest update: February 20, 2025