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).

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

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.

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..

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.

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

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

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 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

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. 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

THIS MAP IS NOT AUTHORITATIVE. SEE TERMS OF USE BELOW.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 19, 2025

Lidar was collected for the Road 46N02-Modoc area of California between October 4th - 5th 2013 for the USFS Pacific Region. Data was collected to provide a highly detailed ground surface dataset to be used for the development of topographic, contour mapping and hydraulic modeling. This dataset covers over 13,000 acres ( over 55 km²)

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.

This dataset is the bare earth lidar data for St. Johns County, Florida, acquired in early January and February of 2004. This data was collected to develop comprehensive countywide base mapping and perform other GIS enhancements to support master drainage planning, transportation planning, preliminary engineering and wetland preservation studies. The surveyed area included all of St. Johns County, approximately 610 square miles. Eighty-seven (87) flight lines of high density lidar data (average GSD is 3.3 feet) were obtained at an altitude of 3000 feet AGL. This data set contains only model keypoints (points that are a thinned data set that is intended to remove extraneous data such as trees and points that are deemed redundant to the final bare earth product) that are classified as ground points. As a result, there are a lower number of points than in a full mass point lidar data set; and it is recommended that the data be downloaded as points and used with a TIN (Triangulated Irregular Network) or similar algorithm to produce a bare earth surface.

This data set is one component of a digital terrain model (DTM) for the SWFWMD Polk District. This record includes information about the LiDAR data for the following SWFWMD tracts: Hampton, Judy, Lake Wales, Peace River (North) and Polk Remainder. All of these tracts are located in Polk County. Please see the Bounding Coordinates for each tract for the location within Polk County. Information that is specific to each tract has been maintained. HAMPTON TRACT This data set is one component of a digital terrain model (DTM) for Hampton Tract, Polk County, Florida encompassing approximately 43 square miles. This dataset is comprised of 48 LiDAR files, based on the DISTRICT 5,000' by 5,000' sheet index system (17951-17958, 18114-18121, 18363-18370, 18526-18533, 18259-18266 and 18594-18601) in the LAS file format. The raw data was collected at an average ground sample distance of 1-meter. Other components of the DTM include: 3-D breaklines along hydrographic features in the Shape file format; lake/pond polygons (in 3D) in the shape file format; obscured area polygons (in 2D) in the Shape file format; and hard/soft breaklines (in 3D) in the Shape file format. Date of Collection: 20060125 Bounding Coordinates of tract: West Bounding Coordinate: -82.035984 East Bounding Coordinate: -81.903345 North Bounding Coordinate: 28.328847 South Bounding Coordinate: 28.238391 JUDY TRACT This data set is one component of a digital terrain model (DTM) for Judy Tract, Polk County, Florida encompassing approximately 12.6 square miles. This dataset is comprised of 14 LiDAR files, based on the DISTRICT 5,000' by 5,000' sheet index system (17632-17636, 17795-17799, and 17959-17962) in the LAS file format. The raw data was collected at an average ground sample distance of 1-meter. Other components of the DTM include a personal geodatabase containing: obscured vegetation polygons; road overpass polygons; road breaklines; soft feature breaklines; water body polygons; coastal shorelines; 1-foot contours; hydrographic feature breaklines, and island polygons in accordance with the SWFWMD 2006 Topographic Database Design. Date of Collection: 20060125 Bounding Coordinates of tract: West Bounding Coordinate: -81.925929 East Bounding Coordinate: -81.848175 North Bounding Coordinate: 28.350110 South Bounding Coordinate: 28.308789 LAKE WALES This data set is one component of a digital terrain model (DTM) for Lake Wales, Polk County, Florida encompassing approximately 10.75 square miles. This dataset is comprised of 12 LiDAR files, based on the DISTRICT 5,000' by 5,000' sheet index system (22365-22367, 22528-22530, 22691-22693 and 22854-22856) in the LAS file format. The raw data was collected at an average ground sample distance of 1-meter. Other components of the DTM include: 3-D breaklines along hydrographic features in the Shape file format; lake/pond polygons (in 3D) in the shape file format; obscured area polygons (in 2D) in the Shape file format; and hard/soft breaklines (in 3D) in the Shape file format. Date of Collection: 20060125 Bounding Coordinates of tract: West Bounding Coordinate: -81.543294 East Bounding Coordinate: -81.509932 North Bounding Coordinate: 27.916603 South Bounding Coordinate: 27.868213 PEACE RIVER (NORTH) This data set is one component of a digital terrain model (DTM) for Peace River North (P692), Polk County, Florida encompassing approximately 1,149 square miles. This dataset is comprised of 1,281 LiDAR files, based on the DISTRICT 5,000' by 5,000' sheet index system in the LAS file format. The raw data was collected at an average ground sample distance of 1-meter. Other components of the DTM include: 3-D breaklines along hydrographic features in the Shape file format; lake/pond polygons (in 3D) in the shape file format; obscured area polygons (in 2D) in the Shape file format; and hard/soft breaklines (in 3D) in the Shape file format. Date of Collection: 20060227 Bounding Coordinates of tract: West Bounding Coordinate: -82.035984 East Bounding Coordinate: -81.903345 North Bounding Coordinate: 28.328847 South Bounding Coordinate: 28.238391 POLK DISTRICT REMAINDER This dataset is one component of a digital terrain model (DTM) for the Southwest Florida Water Management District's FY2007 Remainder Polk District LiDAR Mapping Project and Polk District Contours Project (L672) encompassing approximately 428 square miles in Polk County, Florida. This dataset is comprised of 478 LiDAR files, based on the FL Statewide 5,000' by 5,000' sheet index system in the LAS version 1.1 file format. LiDAR acquisition dates were January 27, January 30 through February 20, 2005. The raw data was collected at an average ground sample distance of 2.1 feet. Other components of the DTM include a personal geodatabase in accordance with the SWFWMD 2006 Topographic Database Design containing: obscured vegetation polygons; road overpass polygons; road breaklines; soft feature breaklines; water body polygons; coastal shorelines; hydrographic features breaklines; island polygons; and 1-foot contours. Final products include FEMA-compliant LIDAR-derived DTM data and 1-foot contours (for cartographic visualization purposes only) meeting or exceeding National Map Accuracy Standards for 2-foot contours. This area is not a tract, but an addition of areas to the Polk District data set. This data set consists of three areas within Polk County. The approximate bounding coordinates for each area are given below. Area 3 borders the east and north sides of the Peace River (North) and Judy Tract data sets. The bounding coordinates for Area 3 are generalized here, but it is actually a multi-sided polygon with many vertices. Date of Collection: 20050127, 20050130-20050220 Bounding Coordinates of Area 1: Area 2 Area 3 West Bounding Coordinate: -82.104809 -81.958339 -81.934587 East Bounding Coordinate: -82.033554 -81.918752 -81.380374 North Bounding Coordinate: 28.314262 28.349889 28.397394 South Bounding Coordinate: 28.171749 28.322179 27.625454

description: 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 .; abstract: 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 .

Road Profile Laser Sensors Market Outlook

The global road profile laser sensors market size was valued at approximately USD 120 million in 2023 and is projected to reach around USD 300 million by 2032, at a CAGR of 10.5% during the forecast period. The growth of this market is driven by the increasing need for advanced road profiling technologies to enhance road safety and maintenance efficiency.

One of the primary growth factors for the road profile laser sensors market is the escalating demand for advanced road safety and maintenance solutions. Governments and private entities globally are increasingly investing in infrastructure projects, necessitating the use of sophisticated technologies for road profiling. Road profile laser sensors provide precise measurements of road surfaces, which are crucial for maintaining high safety standards and improving the longevity of road infrastructure. The advancements in sensor technology, including the development of more accurate and durable laser sensors, are also contributing significantly to market growth.

Another critical growth factor is the increasing adoption of autonomous and connected vehicles. Road profile laser sensors play a pivotal role in the development of these technologies by providing essential data for vehicle navigation systems. The sensors help in mapping and understanding road conditions, thereby enhancing the performance of advanced driver-assistance systems (ADAS). As the automotive industry continues to innovate and integrate more sophisticated systems, the demand for road profile laser sensors is expected to rise substantially.

The expansion of smart city projects globally is further propelling the growth of the road profile laser sensors market. Smart cities aim to utilize advanced technologies to improve urban infrastructure and services, and road profile laser sensors are integral to these initiatives. They help in monitoring and analyzing road conditions in real-time, enabling timely maintenance and reducing the occurrence of road accidents. The integration of these sensors with other smart city technologies provides a comprehensive solution for urban mobility and infrastructure management.

Regional outlook for the road profile laser sensors market indicates significant growth potential across various geographies. North America currently holds the largest market share due to the extensive adoption of advanced road profiling technologies and substantial investments in infrastructure development. However, the Asia Pacific region is expected to witness the highest growth rate, driven by rapid urbanization, increasing government initiatives for smart city projects, and expanding transportation networks. Europe also presents considerable growth opportunities, particularly with the ongoing advancements in automotive technologies and stringent regulations for road safety.

In recent years, the introduction of 3D Line Laser Profile Sensors has revolutionized the way road profiling is conducted. These sensors provide an unprecedented level of detail by capturing intricate three-dimensional data of road surfaces. This capability is particularly beneficial for applications requiring high precision, such as in the development of autonomous vehicles and advanced road maintenance systems. The ability to analyze complex surface characteristics allows for enhanced decision-making and more effective maintenance strategies, ensuring roads are safer and more durable. As infrastructure projects increasingly demand sophisticated technologies, the adoption of 3D Line Laser Profile Sensors is expected to grow, offering significant advantages over traditional methods.

Product Type Analysis

The product type segment of the road profile laser sensors market is categorized into 2D laser sensors and 3D laser sensors. 2D laser sensors are widely used for their ability to provide accurate surface measurements. These sensors are essential for applications that require precise detection of surface irregularities and pavement profiling. The simplicity and cost-effectiveness of 2D laser sensors make them a popular choice for various road profiling tasks, especially in scenarios where basic surface measurements are sufficient.

3D laser sensors, on the other hand, offer a more comprehensive analysis by capturing three-dimensional data of the road surface. This advanced capability allows for detailed profiling and identification of complex surface characteristics,