This dataset contains linework of lineaments mapped on 4 <1-m-resolution lidar datasets and the 10-m-resolution National Elevation Dataset digital elevation models in the Pit River region of northeastern California. Lineaments are classified by confidence in tectonic origin, map certainty, and the ages of the bedrock and surficial deposits they cross.

This data collection of the 3D Elevation Program (3DEP) consists of Lidar Point Cloud (LPC) projects as provided to the USGS. These point cloud files contain all the original lidar points collected, with the original spatial reference and units preserved. These data may have been used as the source of updates to the 1/3-arcsecond, 1-arcsecond, and 2-arcsecond seamless 3DEP Digital Elevation Models (DEMs). The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Lidar (Light detection and ranging) discrete-return point cloud data are available in LAZ format. The LAZ format is a lossless compressed version of the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. Point Cloud data can be converted from LAZ to LAS or LAS to LAZ without the loss of any information. Either format stores 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of geo-referenced x, y coordinates and z (elevation), as well as other attributes for each point. Additonal information about the las file format can be found here: https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities. All 3DEP products are public domain.

Lidar point cloud data with classifications – unclassified (1), ground (2), low vegetation (3), medium vegetation (4), high vegetation (5), buildings (6), low point - noise (7), reserved – model keypoint (8), high noise (18).

This record is for Approval for Access product AfA439. A habitat map derived from airborne data, specifically CASI (Compact Airborne Spectrographic Imager) and LIDAR (Light Detection and Ranging) data.

The habitat map is a polygon shapefile showing site relevant habitat classes. Geographical coverage is incomplete because of limits in data available. It includes those areas where the Environment Agency, Natural England and the Regional Coastal Monitoring Programme have carried out sufficient aerial and ground surveys in England.

The habitat map is derived from CASI multispectral data, LIDAR elevation data and other GIS products. The classification uses ground data from sites collected near to the time of CASI capture. We use ground data to identify the characteristics of the different habitats in the CASI and LIDAR data. These characteristics are then used to classify the remaining areas into one of the different habitats.

Habitat maps generated by Geomatics are often derived using multiple data sources (e.g. CASI, LIDAR and OS-base mapping data), which may or may not have been captured coincidentally. In instances where datasets are not coincidentally captured there may be some errors brought about by seasonal, developmental or anthropological change in the habitat.

The collection of ground data used in the classification has some limitations. It has not been collected at the same time as CASI or LIDAR capture; it is normally within a couple of months of CASI capture. Some variations between the CASI data and situation on site at the time of ground data collection are possible. A good spatial coverage of ground data around the site is recommended, although not always practically achievable. For a class to be mapped on site there must have been samples collected for it on site. If the class is not seen on site or samples are not collected for a class, it cannot be mapped.

No quantitative accuracy assessment has been carried out on the habitat map, although the classification was trained using ground data and the final habitat map has been critically evaluated using Aerial Photography captured simultaneously with the CASI data by the processors and independently by habitat specialists. Please note that this content contains Ordnance Survey data © Crown copyright and database right [2014] and you must ensure that a similar attribution statement is contained in any sub-licences of the Information that you grant, together with a requirement that any further sub-licences do the same.

Mapping Lidar Laser Market Outlook

The global market size for Mapping Lidar Laser in 2023 is estimated to be around USD 2.3 billion, and it is projected to reach approximately USD 7.1 billion by 2032, growing at a CAGR of 13.2% during the forecast period. This growth trajectory is driven by the expanding adoption of Lidar technology in various industries such as construction, transportation, and environmental monitoring, as well as technological advancements and the increasing need for precise geospatial measurements.

One of the primary growth factors in the Mapping Lidar Laser market is the rise in infrastructure development activities globally. Governments and private sectors are heavily investing in smart city projects, which require advanced mapping technologies for urban planning and development. Lidar technology, with its high accuracy and rapid data collection capabilities, is becoming indispensable for creating detailed 3D maps and models. Additionally, the increasing demand for autonomous vehicles, which rely heavily on Lidar systems for navigation and safety, is further propelling the market growth.

Furthermore, the need for efficient corridor mapping and aerial surveying has been driving the market. Lidar technology offers precise topographical data, which is crucial for planning transportation routes, such as highways and railway lines. This technology is also being extensively adopted in the forestry and agriculture sectors for vegetation analysis and land use planning. The ability of Lidar to penetrate through foliage and provide detailed ground surface models makes it a valuable tool in these industries.

Technological advancements in Lidar systems are also contributing significantly to market growth. The development of compact, lightweight, and cost-effective Lidar sensors has made the technology more accessible to a broader range of applications. Innovations such as solid-state Lidar and advancements in data processing algorithms have improved the performance and reduced the costs of Lidar systems, making them an attractive option for various industries. This continuous evolution in technology is expected to sustain the market's growth momentum over the forecast period.

Light Detection and Ranging Devices, commonly known as Lidar, have revolutionized the way we perceive and interact with our environment. These devices utilize laser pulses to measure distances with high precision, creating detailed three-dimensional maps of the surroundings. The ability of Lidar to provide accurate and real-time data has made it an essential tool in various industries, from urban planning to autonomous vehicles. As the technology continues to advance, the integration of Lidar into everyday applications is becoming more seamless, enhancing our ability to monitor and manage complex systems. The growing demand for such devices underscores their critical role in driving innovation and efficiency across multiple sectors.

Regionally, North America is expected to dominate the Mapping Lidar Laser market due to the early adoption of advanced technologies and significant investments in infrastructure projects. The presence of major Lidar system manufacturers and the increasing use of Lidar in autonomous vehicles and environmental monitoring are driving the market in this region. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate due to rapid urbanization, infrastructure development, and the adoption of smart city initiatives by countries such as China and India.

Component Analysis

The Mapping Lidar Laser market by component is segmented into hardware, software, and services. The hardware segment includes Lidar sensors, GPS systems, and IMUs (Inertial Measurement Units). This segment currently holds the largest market share due to the essential role of hardware components in Lidar systems. Continuous innovations in sensor technology, such as the development of solid-state Lidar, are enhancing the performance and reducing the costs of these systems, thereby driving market growth.

Software components are also crucial for the efficient processing and analysis of Lidar data. This segment is expected to grow significantly due to the increasing need for sophisticated data processing algorithms and visualization tools. Software advancements are enabling more accurate and faster data interpretation, which is essential for applications like urban planning and environme

LIDAR data is remotely sensed high-resolution elevation data collected by an airborne collection platform. Using a combination of laser rangefinding, GPS positioning and inertial measurement technologies; LIDAR instruments are able to make highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures and vegetation. This data was collected over a portion of Maui and Oahu, Hawaii with a Leica ALS-40 Aerial Lidar Sensor. Multiple returns were recorded for each pulse in addition to an intensity value. Original contact information: Contact Org: NOAA Office for Coastal Management Phone: 843-740-1202 Email: coastal.info@noaa.gov

Many Ontario lidar point cloud datasets have been made available for direct download by the Government of Canada through the federal Open Government Portal under the LiDAR Point Clouds – CanElevation Series record. Instructions for bulk data download are available in the Download Instructions document linked from that page. To download individual tiles, zoom in on the map in GeoHub and click a tile for a pop-up containing a download link. See the LIO Support - Large Data Ordering Instructions to obtain a copy of data for projects that are not yet available for direct download. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index. Data sizes by project area are listed below. The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The minimum point cloud classes are Unclassified, Ground, Water, High and Low Noise. The data is structured into non-overlapping 1-km by 1-km tiles in LAZ format. This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters, accuracy and sensors vary by project. Some projects have additional classes, such as vegetation and buildings. See the detailed User Guide and contractor metadata reports linked below for additional information, including information about interpreting the index for placement of data orders. Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived). You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Additional DocumentationOntario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX) Ontario Classified Point Cloud (Lidar-Derived) - Tile IndexOntario Lidar Project Extents (SHP) OMAFRA Lidar 2016-18 - Cochrane - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Peterborough - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Lake Erie - Additional Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Metadata (Word)Timmins Lidar 2024 - Additional Metadata (Word)Cataraqui Lidar 2024 - Additional Metadata (Word)Chapleau Lidar 2024 - Additional Metadata (Word)Dryden-Ignace-Sioux Lookout Lidar 2024 - Additional Metadata (Word)Atikokan Lidar 2024 - Additional Metadata (Word) OMAFRA Lidar Point Cloud 2016-18 - Cochrane - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18- Peterborough - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18 - Lake Erie - Lift Metadata (SHP)CLOCA Lidar Point Cloud 2018 - Lift Metadata (SHP)South Nation Lidar Point Cloud 2018-19 - Lift Metadata (SHP)York-Lake Simcoe Lidar Point Cloud 2019 - Lift Metadata (SHP)Ottawa River Lidar Point Cloud 2019-20 - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Huron - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Simcoe - Lift Metadata (SHP)Eastern Ontario Lidar Point Cloud 2021-22 - Lift Medatadata (SHP)DEDSFM Huron-Georgian Bay Lidar Point Cloud 2022-23 - Lift Metadata (SHP)DEDSFM Kawartha Lakes Lidar Point Cloud 2023 - Lift Metadata (SHP)DEDSFM Sault Ste Marie Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Sudbury Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Thunder Bay Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Timmins Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Cataraqui Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Chapleau Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Dryden Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Ignace Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Sioux Lookout Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Northeastern Ontario Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Atikokan Lidar Point Cloud 2024 - Lift Metadata (SHP)GTA 2023 - Lift Metadata (SHP) Data Package SizesLEAP 2009 - 22.9 GBOMAFRA Lidar 2016-18 - Cochrane - 442 GBOMAFRA Lidar 2016-18 - Lake Erie - 1.22 TBOMAFRA Lidar 2016-18 - Peterborough - 443 GBGTA 2014 - 57.6 GBGTA 2015 - 63.4 GBBrampton 2015 - 5.9 GBPeel 2016 - 49.2 GBMilton 2017 - 15.3 GBHalton 2018 - 73 GBCLOCA 2018 - 36.2 GBSouth Nation 2018-19 - 72.4 GBYork Region-Lake Simcoe Watershed 2019 - 75 GBOttawa River 2019-20 - 836 GBLake Nipissing 2020 - 700 GBOttawa-Gatineau 2019-20 - 551 GBHamilton-Niagara 2021 - 660 GBOMAFRA Lidar 2022 - Lake Huron - 204 GBOMAFRA Lidar 2022 - Lake Simcoe - 154 GBBelleville 2022 - 1.09 TBEastern Ontario 2021-22 - 1.5 TBHuron Shores 2021 - 35.5 GBMuskoka 2018 - 72.1 GBMuskoka 2021 - 74.2 GBMuskoka 2023 - 532 GBDigital Elevation Data to Support Flood Mapping 2022-26:Huron-Georgian Bay 2022 - 1.37 TBHuron-Georgian Bay 2023 - 257 GBHuron-Georgian Bay 2023 Bruce - 95.2 GBKawartha Lakes 2023 - 385 GBSault Ste Marie 2023-24 - 1.15 TBSudbury 2023-24 - 741 GBThunder Bay 2023-24 - 654 GBTimmins 2024 - 318 GBCataraqui 2024 - 50.5 GBChapleau 2024 - 127 GBDryden 2024 - 187 GBIgnace 2024 - 10.7 GBNortheastern Ontario 2024 - 82.3 GBSioux Lookout 2024 - 112 GBAtikokan 2024 - 64 GBGTA 2023 - 985 GB StatusOn going: Data is continually being updated Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Digital Surface Model (DSM) has three layers. Two layers come from the rasterisation of the building and vegetation classes among all the points of the LiDAR file .las; and the third layer is the hydrography of the Geographical Reference Information. By applying a suitable colour for each layer, the final product is visualised. ECW file format. ETRS89 reference geodetic system (in the Canary Islands REGCAN95, compatible with ETRS89) and EPSG projection: 3857 throughout the national territory

LiDAR Mapping System Market Outlook

According to our latest research, the global LiDAR Mapping System market size is valued at USD 4.8 billion in 2024 and is expected to reach USD 21.1 billion by 2033, growing at a robust CAGR of 17.9% during the forecast period of 2025 to 2033. The remarkable expansion of this market is primarily driven by the surging demand for high-precision mapping solutions across sectors such as automotive, urban planning, infrastructure, and environmental monitoring. The integration of LiDAR technology with advanced data analytics, the proliferation of autonomous vehicles, and the increasing adoption of UAVs and drones for mapping applications are key factors propelling this marketÂ’s rapid growth trajectory.

The primary growth driver for the LiDAR Mapping System market is the escalating need for accurate and real-time spatial data in numerous industries. The automotive sector, in particular, is witnessing a significant transformation with the advent of ADAS (Advanced Driver Assistance Systems) and autonomous vehicles, which heavily rely on LiDAR technology for navigation, obstacle detection, and environmental perception. Additionally, the widespread adoption of smart city initiatives and infrastructure development projects globally is further amplifying the demand for high-resolution mapping solutions. The ability of LiDAR systems to deliver precise three-dimensional information, even in challenging environments, makes them indispensable for applications ranging from corridor mapping and urban planning to environmental monitoring and resource exploration.

Another crucial growth factor is the technological advancements in LiDAR hardware and software components, which have significantly enhanced the performance, affordability, and versatility of these systems. The transition from traditional mechanical LiDAR to solid-state and MEMS-based LiDAR has resulted in lighter, more compact, and cost-effective solutions, enabling broader adoption in emerging applications such as UAV-based mapping and mobile LiDAR platforms. Furthermore, the integration of artificial intelligence and machine learning algorithms with LiDAR data processing is unlocking new possibilities in automated feature extraction, change detection, and predictive analytics. These innovations are not only improving the efficiency and accuracy of mapping workflows but also expanding the addressable market for LiDAR mapping systems across diverse end-user segments.

The growing emphasis on environmental sustainability and regulatory compliance is also fueling the adoption of LiDAR mapping systems. Governments and environmental agencies are increasingly leveraging LiDAR technology for forest inventory, flood modeling, coastal zone management, and disaster assessment due to its ability to capture detailed topographic information over large areas rapidly. Moreover, the mining, agriculture, and oil & gas sectors are utilizing LiDAR-based solutions to optimize resource management, monitor land use changes, and enhance operational safety. As environmental regulations become more stringent and the need for accurate geospatial data intensifies, the market for LiDAR mapping systems is poised for sustained growth over the coming decade.

The advent of the Mobile Lidar Mapping Vehicle is revolutionizing the way data is collected for various mapping applications. These vehicles, equipped with advanced LiDAR systems, are capable of capturing high-resolution spatial data while in motion, making them ideal for mapping extensive road networks, urban environments, and infrastructure projects. By integrating LiDAR sensors with GPS and IMU units, mobile mapping vehicles provide accurate and real-time geospatial information, which is crucial for applications such as transportation planning, utility management, and smart city development. The ability to collect data rapidly and efficiently, without disrupting traffic or requiring extensive ground-based surveys, is driving the adoption of mobile LiDAR solutions across sectors. As technology continues to advance, the role of mobile LiDAR mapping vehicles in supporting infrastructure modernization and urban planning initiatives is expected to grow significantly.

Regionally, North America currently dominates the LiDAR Mapping System market, accounting for the largest revenue share in 202

Zoom in on the map above and click your area of interest or use the Tile Index linked below to determine which package(s) you require for download.The DSM data is available in the form of 1-km by 1-km non-overlapping tiles grouped into packages for download.This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters and sensors may vary by project. See the detailed User Guide linked below for additional information.You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Now also available through a web service which exposes the data for visualization, geoprocessing and limited download. The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connectionusing the REST endpoint URL. The service draws using the Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Service Endpointshttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServerhttps://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer (Government of Ontario Internal Users)Additional DocumentationOntario DSM (Lidar-Derived) - User Guide (DOCX) OMAFRA Lidar 2016-2018 - Cochrane - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Peterborough - Additional Contractor Metadata (PDF)OMAFRA Lidar 2016-2018 - Lake Erie - Additional Contractor Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Contractor Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Contractor Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Contractor Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Contractor Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Contractor Metadata (Word)Timmins Lidar 2024 - Additional Contractor Metadata (Word)Cataraqui Lidar 2024 - Additional Metadata (Word)Chapleau Lidar 2024 - Additional Metadata (Word)Dryden-Ignace-Sioux Lookout Lidar 2024 - Additional Metadata (Word)Atikokan Lidar 2024 - Additional Metadata (Word) Ontario DSM (Lidar-Derived) - Tile Index (SHP)Ontario Lidar Project Extents (SHP)Product PackagesDownload links for the Ontario DSM (Lidar-Derived) (Word)Projects:LEAP 2009GTA 2014-18OMAFRA 2016-18CLOCA 2018South Nation CA 2018-19Muskoka 2018-23York-Lake Simcoe 2019Ottawa River 2019-20Ottawa-Gatineau 2019-20Lake Nipissing 2020Hamilton-Niagara 2021Huron Shores 2021Eastern Ontario 2021-22OMAFRA Lake Huron 2022OMAFRA Lake Simcoe 2022Belleville 2022Digital Elevation Data to Support Flood Mapping 2022-26Huron-Georgian Bay 2022-23Kawartha Lakes 2023Sault Ste Marie 2023-24Sudbury 2023-24Thunder Bay 2023-24Timmins 2024Cataraqui 2024Chapleau 2024Dryden 2024Ignace 2024Northeastern Ontario 2024Sioux Lookout 2024Atikokan 2024Greater Toronto Area Lidar 2023StatusOn going: Data is continually being updated Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

The LiDAR technology market for mapping is booming, with a projected CAGR of 15% reaching $7.65 Billion by 2033. Discover key trends, applications (autonomous vehicles, architecture, mining), leading companies, and regional market share analysis in this comprehensive market report.

LIDAR data is remotely sensed high-resolution elevation data collected by an airborne collection platform. Using a combination of laser rangefinding, GPS positioning and inertial measurement technologies; LIDAR instruments are able to make highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures and vegetation. This data was collected at submeter resolution to provide nominal 1m spacing of collected points. Two returns were recorded for each pulse in addition to an intensity value. Original contact information: Contact Org: NOAA Office for Coastal Management Phone: 843-740-1202 Email: coastal.info@noaa.gov

The UAV LiDAR Mapping market is experiencing robust growth, projected to reach $108 million in 2025 and maintain a compound annual growth rate (CAGR) of 8.0% from 2025 to 2033. This expansion is driven by several key factors. Increasing demand for high-accuracy geospatial data across diverse sectors like land surveying, forestry, and urban planning fuels the adoption of UAV LiDAR technology. The technology's ability to provide detailed 3D models and accurate measurements surpasses traditional methods in efficiency and cost-effectiveness, further boosting market growth. Furthermore, advancements in sensor technology, improved data processing capabilities, and the decreasing cost of drones are contributing to wider accessibility and affordability, making UAV LiDAR a viable solution for a broader range of applications. The market segmentation reveals strong demand across various applications, with land surveys, forestry, and infrastructure projects (highways, quarries, civil structures) representing significant revenue streams. Specific applications like digital twin creation and volumetric analysis are experiencing particularly rapid growth, reflecting a shift towards more sophisticated data utilization. Geographic expansion is also a significant driver, with North America and Europe currently leading the market but substantial growth potential existing in Asia-Pacific and other developing regions. The competitive landscape is characterized by a mix of established players and emerging companies, each offering specialized solutions and services. The presence of both large-scale technology providers and niche drone service providers caters to the varied needs of the industry. However, challenges remain, including regulatory hurdles related to drone operations, the need for skilled professionals to operate and interpret data, and potential weather-related constraints on data acquisition. Despite these challenges, the long-term outlook for the UAV LiDAR Mapping market remains positive, driven by continuous technological innovations and growing demand for efficient, accurate geospatial data across a widening spectrum of applications. The market's growth will likely be further fueled by increased government investments in infrastructure projects and the growing adoption of smart city initiatives.

Virginia LiDARThe Virginia LiDAR Inventory Web Mapping Application provides access to LiDAR point cloud and individual project metadata collected in the Commonwealth of Virginia according to the USGS 3DEP specification. Data is obtained from NOAA and USGS data portals. LiDAR Point Clouds are compressed for file storage and transfer. Informational Access Type:1) LiDAR Project Metadata: To download individual LiDAR project Metadata, click on a LiDAR inventory polygon for link to the host FTP site. Once at the host site, locate appropriate directory and .zip file to receive project documentation and accompanying project files. For use within ArcGIS, the geospatial grid and inventory data powering the VGIN LiDAR download inventory services can be downloaded under conversion and analysis resources below.2) LiDAR Point Clouds (Single): To download individual tiles, zoom in on the map until the tile grid appears. The VGIN Composite Geocoding service is available to use when querying by physical address, feature, or community anchor institution name. Click a tile to identify grid information for individual LiDAR Point clouds. Columns note where the LiDAR is hosted and what format is available for download. In many instances, multiple results are returned due to multiple file formats and flight years. If LiDAR data is missing spatial reference information please refer to the metadata in step 1 above. Tile grids are stacked so you will need to scroll through selections:3) LiDAR Point Clouds (Bulk): To download multiple files in a single FTP directory folder, which can be a necessity in many instances, consider the use of a multi-file download manager plugin to use with your browser in conjunction with the URLs provided on the LiDAR inventory polygon. If LiDAR data is missing spatial reference information please refer to the metadata in step 1 above. For use within ArcGIS, the geospatial grid and inventory data powering the VGIN LiDAR Download Inventory Services can be downloaded under conversion and resources below.Conversion and Resources:Convert to LAS from USGS/NOAA hosted .LAZ filesDownload LiDAR Inventory Data Project FootprintsDownload LiDAR Inventory Tile GridContact:For questions about the data please contact USGS For questions about the application please contact vbmp@vdem.virginia.gov

Mobile LiDAR Mapping Vehicle Market Outlook

According to our latest research, the global mobile LiDAR mapping vehicle market size reached USD 3.2 billion in 2024, exhibiting robust momentum driven by technological advancements and increasing demand for high-precision geospatial data across various industries. The market is expected to expand at a CAGR of 14.6% from 2025 to 2033, significantly outpacing many adjacent sectors. By 2033, the market is projected to reach USD 10.2 billion, underlining the transformative impact of LiDAR mapping on sectors such as transportation, urban planning, and agriculture. Key growth factors include rising infrastructure development, smart city initiatives, and the proliferation of autonomous vehicles, all of which necessitate accurate real-time mapping and data acquisition.

One of the primary growth drivers for the mobile LiDAR mapping vehicle market is the surge in demand for high-resolution, three-dimensional geospatial data in infrastructure and urban development projects. Governments and private entities are increasingly leveraging LiDAR technology for efficient planning, monitoring, and management of roads, bridges, and utilities. The ability of mobile LiDAR systems to rapidly capture detailed environmental data with high accuracy has revolutionized the surveying and mapping landscape, allowing for faster project completion and reduced operational costs. This adoption is further accelerated by the increasing complexity of modern infrastructure projects, which require precise and up-to-date geospatial information to minimize errors and optimize resource allocation.

Another significant growth factor is the integration of LiDAR mapping technology in the automotive industry, particularly in the development of autonomous vehicles and advanced driver-assistance systems (ADAS). Mobile LiDAR mapping vehicles provide real-time, high-fidelity environmental perception, which is crucial for the safe and efficient operation of self-driving cars and commercial fleets. The automotive sector’s push towards automation and safety compliance is driving substantial investments in LiDAR-equipped mapping vehicles, not only for road mapping but also for continuous updates of navigation databases. Additionally, the rise of UAVs and drones equipped with LiDAR sensors is expanding the market’s reach into previously inaccessible or hazardous environments, such as mountainous terrains, forests, and disaster zones.

The adoption of mobile LiDAR mapping vehicles is also being propelled by advancements in sensor technology, data processing algorithms, and cloud-based storage solutions. Modern LiDAR systems are becoming more compact, energy-efficient, and cost-effective, allowing for their deployment across a wider range of vehicle types and applications. Enhanced data processing capabilities enable real-time analysis and visualization of massive datasets, facilitating faster decision-making and improved operational efficiency. Furthermore, the integration of artificial intelligence and machine learning is enabling automated feature extraction and classification, further expanding the utility of LiDAR mapping in sectors such as agriculture, mining, and construction.

From a regional perspective, North America currently dominates the mobile LiDAR mapping vehicle market, accounting for the largest revenue share in 2024, driven by robust infrastructure investments, technological innovation, and strong presence of leading market players. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, smart city projects, and increasing adoption of advanced mapping technologies in countries such as China, Japan, and India. Europe also holds a significant share, supported by stringent regulatory standards for transportation safety and environmental monitoring. The Middle East & Africa and Latin America, while still nascent, are witnessing growing interest in LiDAR mapping for resource management and infrastructural development, indicating substantial future potential.

Component Analysis

The &

The UAV LiDAR Mapping market is experiencing robust growth, driven by the increasing demand for high-accuracy geospatial data across diverse sectors. Applications such as land surveying, forestry management, and infrastructure development are significantly benefiting from the efficiency and cost-effectiveness of UAV-based LiDAR solutions. The market's expansion is fueled by advancements in sensor technology, leading to improved data resolution and accuracy, and a reduction in processing time. Furthermore, the decreasing cost of UAV platforms and the rising availability of user-friendly data processing software are making this technology accessible to a wider range of users, including smaller surveying firms and research institutions. The integration of AI and machine learning in data processing workflows further enhances the value proposition, enabling automated feature extraction and analysis, accelerating project completion, and reducing reliance on manual interpretation. Despite the significant growth potential, the market faces some challenges. Regulatory hurdles related to UAV operations, including airspace restrictions and licensing requirements, can hinder wider adoption. The need for skilled professionals capable of operating UAVs, processing LiDAR data, and interpreting the results presents another barrier. However, these challenges are gradually being addressed through the development of standardized operating procedures, improved training programs, and the emergence of user-friendly software solutions. The market's segmentation, encompassing various applications (land surveys, forestry, etc.) and LiDAR data types (3D visualization, digital twin creation, etc.), provides numerous opportunities for specialized service providers and technology vendors. The substantial growth in the Asia-Pacific region, driven by infrastructural development projects and government initiatives, is a significant factor driving overall market expansion. We anticipate continued market expansion fueled by technological innovation and increasing industry demand, albeit at a rate moderated by regulatory and skilled labor considerations.

These files contain classified topo/bathy lidar data generated from data collected by the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with simultaneous topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. Native lidar data is not gen...

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. The dendrometric lidar map presents various dendrometric characteristics useful in particular in forest planning. It is a product in vector format that is complementary to the results of forest compilations found in the Original Ecoforest Map and Current Inventory Results and in the Results of forest compilations by forel. The geometric entities defined from the lidar data are at a finer scale than those in the ecoforest map. The main variables predicted and accessible in the product are as follows: + Usable volume per hectare by species, species group and certain diameter groups + Volume per hectare distributed by product for certain species groups + Basal area and number of stems per hectare for certain species groups + Average usable volume per rod and average diameter for certain species groups The volumes compiled in the lidar dendrometric map are variables distinct from the gross standing market volume predicted in the other forest compilation results, in the [Cubing rate and prediction models] ] (https://www.donneesquebec.ca/recherche/dataset/tarif-de-cubage) and for the stems counted in the sample plots of the ecoforest inventory of southern Quebec, for example in the Temporary sample plots of the fifth inventory. This distinct volume is here qualified as “usable” and it excludes woody material between 9.1 cm in diameter without bark and 9.1 cm with bark. The published literature clarifies the differences between volume variables. This product is available for territories (planning unit, private forest development agency or residual forest territory) with a lidar acquisition and affecting the bioclimatic domains of fir to yellow birch, fir to white birch and spruce moss. Product coverage is not complete and will evolve over the years depending on the lidar acquisition. _ ⚠️ Notes: _ It is possible to use the lidar dendrometric data preparation tool to study one or more sectors at a finer scale than that of the ecoforest map. The Lidar dendrometric tool user guide presents the methodology for its application to meet the needs of operational forest harvest planning.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The size of the 3D LiDAR Mapping market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

This data was collected by the Geological Survey Ireland, the Department of Culture, Heritage and the Gaeltacht, the Discovery Programme, the Heritage Council, Transport Infrastructure Ireland, New York University, the Office of Public Works and Westmeath County Council. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution varies depending on survey requirements. Resolutions for each organisation are as follows: GSI – 1m DCHG/DP/HC - 0.13m, 0.14m, 1m NY – 1m TII – 2m OPW – 2m WMCC - 0.25m Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. The grid cell size varies depending on the organisation that collected it. GSI data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared. .hidden { display: none }