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.

Light Detection and Ranging (lidar) is a technology used to create high-resolution models of ground elevation with a vertical accuracy of 10 centimeters (4 inches).  rnrnFEMA collects lidar elevation data to support flood mapping. USGS is the primary Federal steward of lidar data. FEMA archives lidar data for FEMA projects where USGS does not manage the Lidar data collection. rnrnDatapoints include ground elevation models and vertical metrics for ground elevation.

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

In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications and construction plans. This dataset is the real-time kinematic (RTK) data collected during lidar data acquisition. Data were used to check the accuracy of collected lidar data.

The Point Cloud LiDAR Data Processing Software market is experiencing robust growth, driven by the increasing adoption of LiDAR technology across various sectors. The surge in demand for accurate 3D spatial data in applications like autonomous vehicles, precision agriculture, infrastructure management, and urban planning is fueling market expansion. Technological advancements, including the development of sophisticated algorithms for point cloud processing and the integration of AI and machine learning capabilities, are enhancing the efficiency and accuracy of these software solutions. The market is segmented by software type (e.g., point cloud editing, registration, classification, and modeling software), deployment mode (cloud-based and on-premise), and end-user industry. While competition is intense among established players like Trimble, Bentley Systems, Leica Geosystems, Autodesk, and FARO, the market also presents opportunities for specialized niche players focusing on specific industry applications or innovative processing techniques. The global market is geographically diverse, with North America and Europe currently holding significant market share due to early adoption and technological advancements. However, rapid growth is anticipated in Asia-Pacific and other emerging regions driven by infrastructure development and increasing government investments in digitalization initiatives. The forecast period (2025-2033) projects sustained growth, potentially exceeding a Compound Annual Growth Rate (CAGR) of 15%, reflecting the continued integration of LiDAR data processing into mainstream workflows. Challenges remain, including the high cost of LiDAR data acquisition and processing, the complexity of software solutions, and the need for skilled professionals to operate and interpret the results. Nevertheless, ongoing innovation and the increasing affordability of LiDAR technology are mitigating these challenges, contributing to the market's positive outlook. The competitive landscape is dynamic, with both established players and new entrants continually seeking to improve software features, expand their market reach, and enhance customer support. Strategic partnerships and acquisitions are expected to play a significant role in shaping the market's future trajectory.

The Airborne LiDAR market is experiencing robust growth, driven by increasing demand across various sectors. A compound annual growth rate (CAGR) of 18% from 2019 to 2024 suggests a significant expansion, with projections indicating continued strong performance through 2033. Key drivers include the rising need for high-accuracy geospatial data in infrastructure development, precision agriculture, and environmental monitoring. The market is segmented by LiDAR type (topographic and bathymetric), offering (hardware and services), and end-user industry (aerospace & defense, mining, forestry & precision agriculture, corridor mapping, oil & gas, and others). The increasing adoption of advanced LiDAR technologies, including those with improved range and resolution capabilities, is further fueling market expansion. The integration of LiDAR data with other geospatial technologies, such as GIS and imagery, enhances its utility across diverse applications, resulting in greater market penetration. While some restraints may exist related to high initial investment costs and the need for specialized expertise, the overall market outlook remains positive due to the numerous applications and benefits associated with Airborne LiDAR technology. We estimate the 2025 market size to be approximately $2.5 Billion (based on an assumed 2024 market size extrapolation using the provided CAGR and acknowledging this is an estimation). Competition is fierce, with both established players like Teledyne Technologies and Leica Geosystems and emerging companies contributing to innovation and market expansion. The geographic distribution of market share will likely see continued growth in the Asia-Pacific region driven by infrastructural development and economic expansion. The North American market currently holds a significant share, fueled by strong government investments in infrastructure projects and technological advancements. However, the Asia-Pacific region is expected to demonstrate faster growth in the coming years due to increasing investments in infrastructure, urban development, and precision agriculture. Europe and other regions are also showing steady growth, albeit at a possibly slower rate than the Asia-Pacific region. The competitive landscape comprises a mix of large multinational corporations and specialized service providers, each offering unique solutions and technological advantages. The continuous development of more efficient and affordable LiDAR systems is poised to further democratize access to this vital technology, unlocking new growth potential across different segments and geographical markets. The long-term outlook is promising, with sustained demand expected to drive considerable market expansion in the forecast period, 2025-2033. Recent developments include: September 2022 - Leica Geosystems, a division of Hexagon, has introduced the Leica DMC-4, a highly efficient airborne imaging sensor with unrivaled image quality for various applications and complex mapping environments. The new system continues Leica Geosystems' tradition of combining industry-leading optics with precision mechanics to deliver the best mapping performance. The sensor enhances image fidelity by combining the CMOS-based Leica MFC150 camera module with Leica Geosystems' proprietary mechanical forward-motion compensation., May 2022 - 95West Aerial Mapping provides innovative data collection by combining an UltraCam Eagle Mark 3 aerial camera system with a Riegl VQ-1560 II-S airborne laser scanner in a single aircraft. Simultaneous operations using TopoFlight Systems management software cut flight time in half compared to separate missions. The four-band large-format imagery and LiDAR point cloud are perfectly in sync., January 2021 - Fugro acquired Geo-data for the project earlier this year using concurrent airborne topographic and bathymetric lidar systems. This 'topobathy' approach will ensure accurate and seamless data collection across the entire 415 km sq. project area, including nearshore and coastal areas.. Key drivers for this market are: Advancements in Drone Technology, Increasing Need for Robust Surveillance Systems across Various Industries. Potential restraints include: Advancements in Drone Technology, Increasing Need for Robust Surveillance Systems across Various Industries. Notable trends are: Aerospace & Defense to Hold the Largest Share.

The UAV Mapping Laser LiDAR market is experiencing robust growth, driven by increasing demand for high-accuracy geospatial data across diverse sectors. Applications range from precision agriculture and infrastructure monitoring to urban planning and environmental surveying. The market's expansion is fueled by advancements in LiDAR sensor technology, miniaturization of UAV platforms, and decreasing costs associated with data acquisition and processing. While precise market size figures for 2025 are unavailable, a reasonable estimation, considering typical market growth patterns in this sector and referencing comparable technologies, places the market value at approximately $500 million. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% over the forecast period (2025-2033), the market is projected to reach a significant size by 2033. This growth trajectory is further supported by the increasing adoption of autonomous flight capabilities and the development of sophisticated data analytics solutions that enhance the usability and value of the collected LiDAR data. Several factors are contributing to this expansion, including the rising need for precise measurements in various industries, enhanced data processing capabilities, and government initiatives promoting the use of advanced surveying techniques. However, challenges remain, such as regulatory hurdles surrounding UAV operations, the high initial investment cost for LiDAR systems, and potential limitations in data accuracy under certain environmental conditions. Despite these restraints, the overall market outlook for UAV Mapping Laser LiDAR remains highly positive, presenting significant opportunities for established players and new entrants alike. Continued technological innovation and decreasing operational costs will further drive market penetration and accelerate growth in the coming years. UAV Mapping Laser LiDAR Market Report: A Comprehensive Analysis This report provides a detailed analysis of the rapidly expanding UAV mapping laser LiDAR market, projecting a market value exceeding $2 billion by 2028. It delves into key technological advancements, market trends, competitive landscapes, and growth opportunities within this dynamic sector. The report leverages insights from leading players like Velodyne, Phoenix LiDAR Systems, Geodetics, and Heliceo, examining various market segments to offer a comprehensive understanding of this evolving field.

The Environment Agency LIDAR Ground Truth surveys dataset is an archive of elevation points and attribute information that have been independently surveyed to verify the accuracy of the EA's LIDAR timestamped surveys. Captured by various independent surveyors, a ground truth survey is a collection of a few hundred points captured on a flat, unambiguous surface such as a tarmac car park or tennis court using GPS. Each ground truth point has an accuracy of +/-3cm R.M.S.E and contains attribute information such as the date of survey, surface type, survey method and transformation and geoidal models used. A ground truth survey may potentially be used for multiple LIDAR surveys provided it is less than 5 years old, or 3 years for coastal projects.

The LIDAR timestamped survey is compared against the ground truth survey to assess the Root Mean Square Error (R.M.S.E), standard deviation and random error of the LIDAR. All LIDAR surveys must report an error of less than +/-15cm RMSE and 10cm for standard deviation and random error to pass quality control. For the specific ground truth results for a LIDAR survey please contact us.

DOGAMI has been supervising and coordinating the collection of large swaths of high resolution, high accuracy lidar data in Oregon and adjacent states since 2006. Following a successful 2500 mi2 consortium effort in the Portland urban area, the Oregon legislature designated DOGAMI as the lead agency for lidar acquisition in Oregon. DOGAMI used a nationwide selection process that resulted in a state price agreement (OPA 8865) with Watershed Sciences Inc. of Corvallis, Oregon. The price agreement specifies data collection (8 pulse/m2, Zerror < 12cm RMSE) and product standards (LAS points, 3ft or 1m bare earth and highest hit DEMs, 1.5ft intensity images, metadata) with a pre-determined unit cost to DOGAMI based on the size of the project area. Since developing OPA 8865 in April 2008, DOGAMI has ordered 13 large lidar flights, totaling 17,500 mi2, has taken final delivery of 16,000 mi2 of data. Funding for these projects has come from consortia organized by DOGAMI that include several dozen Federal, State and local government agencies, non-profits and public utilities. The data quality for all projects that DOGAMI has completed under OPA 8865 has been consistently excellent, substantially exceeding the minimum specifications. All DOGAMI lidar data is in the public domain, please reference DOGAMI as the data source. All DOGAMI lidar program data are systematically evaluated for: Completeness and useability by loading all files; swath to swath consistency by using TerraMatch to compare elevations of millions of coincident points from adjacent swaths, all values to date < 5cm; absolute vertical accuracy by comparing delivered DEMs to an large independent set of RTK GPS control points collected by DOGAMI, all values to date < 7cm RMSE; grid artifacts by visual examination of hillshade and slopeshade images of all bare earth and highest hit DEMs.

The dataset is captured over Samford Ecological Research Facility (SERF), which is located within the Samford valley in south east Queensland, Australia. The central point of the dataset is located at coordinates: 27.38572oS, 152.877098oE. The Vegetation Management Act 1999 protects the vegetation on this property as it provides a refuge to native flora and fauna that are under increasing pressure caused by urbanization.The hyperspectral image was acquired by the SPECIM AsiaEAGLE II sensor on the second of February, 2013. This sensor captures 252 spectral channels ranging from 400.7nm to 999.2nm. The last five channels, i.e., channels 248 to 252, are corrupted and can be excluded. The spatial resolution of the hyperspectral data was set to 1m.The airborne light detection and ranging (LiDAR) data were captured by the ALTM Leica ALS50-II sensor in 2009 composing of a total of 3716157 points in the study area: 2133050 for the first return points, 1213712 for the second return points, 345.736 for the third return points, and 23659 for the fourth return points.The average flight height was 1700 meters and the average point density is two points per square meter. The laser pulse wavelength is 1064nm with a repetition rate of 126 kHz, an average sample spacing of 0.8m and a footprint of 0.34m. The data were collected up to four returns per pulse and the intensity records were supplied on all pulse returns.The nominal vertical accuracy was ±0.15m at 1 sigma and the measured vertical accuracy was ±0.05m at 1 sigma. These values have been determined from check points contrived on an open clear ground. The measured horizontal accuracy was ± 0.31m at 1 sigma.The obtained ground LiDAR returns were interpolated and rasterized into a 1m×1m digital elevation model (DEM) provided by the LiDAR contractor, which was produced from the LiDAR ground points and interpolated coastal boundaries.The first returns of the airborne LiDAR sensor were utilized to produce the normalized digital surface model (nDSM) at 1m spatial resolution using Las2dem.The 1m spatial resolution intensity image was also produced using Las2dem. This software interpolated the points using triangulated irregular networks (TIN). Then, the TINs were rasterized into the nDSM and the intensity image with a pixel size of 1m. The intensity image with 1m spatial resolution was also produced using Las2dem.The LiDAR data were classified into ground" andnon-ground" by the data contractor using algorithms tailored especially for the project area. For the areas covered by dense vegetation, less laser pulse reaches the ground. Consequently, fewer ground points were available for DEM and nDSM surfaces interpolation in those areas. Therefore, the DEM and the nDSM tend to be less accurate in these areas.In order to use the datasets, please fulfill the following three requirements:

1) Giving an acknowledgement as follows:

The authors gratefully acknowledge TERN AusCover and Remote Sensing Centre, Department of Science, Information Technology, Innovation and the Arts, QLD for providing the hyperspectral and LiDAR data, respectively. Airborne lidar are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+LidarAirborne hyperspectral are from http://www.auscover.org.au/xwiki/bin/view/Product+pages/Airborne+Hyperspectral

2) Using the following license for LiDAR and hyperspectral data:

http://creativecommons.org/licenses/by/3.0/3) This dataset was made public by Dr. Pedram Ghamisi from German Aerospace Center (DLR) and Prof. Stuart Phinn from the University of Queensland. Please cite: In WORD:Pedram Ghamisi and Stuart Phinn, Fusion of LiDAR and Hyperspectral Data, Figshare, December 2015, https://dx.doi.org/10.6084/m9.figshare.2007723.v3In LaTex:@article{Ghamisi2015,author = "Pedram Ghamisi and Stuart Phinn",title = "{Fusion of LiDAR and Hyperspectral Data}",journal={Figshare},year = {2015},month = {12},url = "10.6084/m9.figshare.2007723.v3",

}

The SEWRPC Racine county project is to provide high accuracy bare-earth processed LiDAR data for the generation of 2 ft contours. The project consisted of acquisition, post-processing, and classification of LiDAR data. The classified bare-earth data achieved the following: All areas shall be collected at a nominal pulse spacing (NPS) of 1.3 meters based on Wisconsin State Plane South, related to the North American Datum of 1927 (NAD 27), National Geodetic Vertical Datum (NGVD 29) . Vertical accuracy was to achieve a RMSE Z of 0.50 ft (95% confidence level of less than 0.980 ft) or better in the "Open Terrain" land cover category for all areas.

Accuracy statement is based on the area of moderate to flat terrain. Diminished accuracies are to be expected in areas in dense vegetation. The accuracy of the LiDAR data as tested met the vertical accuracy or better, however, derived products may be less accurate in areas of dense vegetation due to a lesser number of points defining the bare-earth in these areas.

Ayres Associates provided Iron County, Wisconsin, with lidar based topographic mapping services in the spring of 2015 as part of WROC. The LiDAR data was collected on 2015/04/15 to 2015/04/17 using an Optech Orion H300 sensor mounted in a fixed-wing aircraft. LiDAR data was collected to support the generation of 2-foot contours to meet FEMA vertical accuracy standards. The LiDAR data was delive...

Market Analysis for Point Cloud LiDAR Data Processing Software The global point cloud LiDAR data processing software market is projected to reach USD 1,666.7 million by 2033, exhibiting a CAGR of 15.9% from 2025 to 2033. The increasing adoption of LiDAR technology in architecture, land surveying, and other industries, coupled with advancements in artificial intelligence (AI) and machine learning (ML) algorithms for data processing, are driving the market growth. The need for accurate and detailed 3D representations of physical spaces and the benefits of LiDAR in generating point clouds for precise modeling and visualization are further fueling market expansion. Key market trends include the adoption of cloud-based solutions, which offer flexibility, scalability, and reduced infrastructure costs. The integration of AI and ML in data processing is automating tasks, enhancing accuracy, and improving efficiency. Moreover, the growing demand for smart city initiatives and infrastructure development is creating new opportunities for the use of point cloud LiDAR data processing software in urban planning, asset management, and transportation systems. Companies in the market include Trimble, Bentley Systems, Leica Geosystems AG, Autodesk, and FARO, who are investing in research and development to provide innovative solutions that meet the evolving needs of their customers.

Click here to access the data directly from the Illinois State Geospatial Data Clearinghouse.

These lidar data are processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles; used to create Reflectance Images, 3D breaklines and hydro-flattened DEMs as necessary. Geographic Extent: Lake county, Illinois covering approximately 466 square miles. Dataset Description: WI Kenosha-Racine Counties and IL 4 County QL1 Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a derived nominal pulse spacing (NPS) of 1 point every 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, U.S Survey Feet and vertical datum of NAVD88 (GEOID12B), U.S. Survey Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles, as tiled Reflectance Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema. Ground Conditions: Lidar was collected April-May 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Ayers established a total of 66 ground control points that were used to calibrate the lidar to known ground locations established throughout the WI Kenosha-Racine Counties and IL 4 County QL1 project area. An additional 195 independent accuracy checkpoints, 116 in Bare Earth and Urban landcovers (116 NVA points), 79 in Tall Grass and Brushland/Low Trees categories (79 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.

Users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data.

These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications.

Link Source: Illinois Geospatial Data Clearinghouse

During the Fall of 2016 AGRC and the Utah Geological Survey acquired ~205 square miles of 8 points per meter Quality Level 1 LiDAR of The Frontier Observatory for Research in Geothermal Energy (FORGE) area around Milford, Utah in Beaver and Millard Counties in western Utah. The 0.5 meter resolution bare earth DEMs and first-return/highest-hit DSMs in .img format have a 10.0cm vertical RMSE accuracy and are available for download. The LAS classified point clouds are also available by request Rick Kelson from AGRC at RKelson@utah.gov or The National Map. This elevation data was collected between October 26 and November 3, 2016 and has a UTM NAD83 (2011) zone 12 north meter NAVD88(GEOID12) projection.

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

The Environment Agency National LIDAR Programme provides accurate elevation data at 1m spatial resolution for all of England.

In 2017 we divided the country into 302 survey blocks covering all of England which were subsequently captured during the winter months (approximately November to April each year) between January 2017 and February 2023. These are known as our 'Phase 1' national lidar programme surveys.

Subsequently we have undertaken repeat surveys of specific blocks based on the on-going requirements for upto date elevation data. Each repeat survey block is given a new incrementing phase number, for example the second time we capture a block this is that blocks 'phase 2' whilst the 3rd time will be 'phase 3'. There is not curretly a plan to capture all the origianl phase 1 survey blocks over a rolling programme with repeat surveys be based on the requirements for upto date elevation data for an area.

All data is published through the DEFRA Data Services survey portal on a quartely on-going bases and a number of different products area available for each survey block. All products are available in 5km tiles aligned to the ordnance survey grid. The tiles are named by the unique survey id, OS grid reference and the first and last survey date of the survey id (P_XXXXX_OSOSOS_SDFLOWN_EDFLOWN.*). The surface models are available in GeoTiff raster format whilst the point cloud is available in *.laz. An index catalogue is also available with provides survey specific information about each tile.

Outlined below is a description of each product that is available for each survey block:

LIDAR Point Cloud: is the discrete LIDAR returns that are used in the creation of the surface models. Supplied in *.laz format they the discrete LIDAR returns have been classified into ground, low, medium and high vegetation classes using an automated classification process.

Digital Surface Model(s) (DSM) are created from the last or only LIDAR pulse returned to the sensor and contains all ground and surface objects.

Digital Terrain Model(s) (DTM) is created from the last return LIDAR pulse classified as ground, filtering out surface objects. Manual filtering is undertaken on the DTM to improve the automated classification routines to produce a most likely ground surface model. Areas of no data, such as water bodies, are also filled to ensure there are no gaps in the model.

First Return Digital Surface Model(s) (FZ DSM) is created from the either the first or only LIDAR pulse returned to the sensor and contains all ground and surface objects. It is more likely to return elevations from the top or near top of trees and the edges of buildings. It can often be used in canopy height modelling and production of building outlines.

Intensity Surface Model(s) (Int DSM) is a measure of the amount of laser light from each laser pulse reflecting from an object. This reflectivity is a function of the near infrared wavelength used and varies with the composition of the surface object reflecting the return and angle of incidence.The intensity surface model produces a grayscale image where darker surfaces such as roads reflect less light than other surfaces such as vegetation.

Light Detection and Ranging (LIDAR) is an airborne mapping technique, which uses a laser to measure the height of the terrain and surface objects on the ground such as trees and buildings. Our LIDAR point cloud product is a collection of hundreds of millions, or sometimes billions of highly accurate 3-dimensional x,y,z points and component attributes. Our gridded raster products such as our digital surface and terrain models are derived from this point cloud. The component attributes a point cloud contains can provide valuable additional information to supplement elevation and can enable the user to make bespoke raster products such as canopy height models or intensity rasters.

Supplied as individual LAZ files labeled by Ordinance Survey grid reference the point cloud is available for surveys going back to 2006. Historic data are available for some areas where we have carried out repeat surveys, such as in the coastal zone for monitoring change. Although the DSM and DTM products are derived from the point cloud data there may not necessarily be a matching point cloud for each surface model due to historic data archiving processes. All LIDAR data has a vertical accuracy of +/-15cm RMSE.

Data is available in 5km download zip files for each year of survey. Within each downloaded zip file are LAZ files aligned to the Ordinance Survey grid. The size of each tile is dependent upon the spatial resolution of the data. Coverage metadata files showing data extent are also available. The coverage files contain metadata for each tile including the start and end date flown of a survey, and what additional component information the point cloud contains.

To find out more about LIDAR and the various surface models we produce please read our story map

This metadata record is for Approval for Access product AfA457. Attribution statement: © Environment Agency copyright and/or database right 2019. All rights reserved. Attribution statement: © Environment Agency copyright and/or database right 2019. All rights reserved.

Airborne LiDAR Market size was valued at USD 1.35 Billion in 2023 and is projected to reach USD 4.05 Billion by 2030, growing at a CAGR of 17% during the forecast period 2024-2030.

Global Airborne LiDAR Market Drivers

The market drivers for the Airborne LiDAR Market can be influenced by various factors. These may include:

Infrastructure Development and Urban Planning: Projects involving infrastructure development, urban planning campaigns, and the requirement for precise topographic data are the main drivers of the need for precise and comprehensive geospatial data. For such initiatives, the collection of high-resolution data is greatly aided by the use of airborne LiDAR. Increased Use in Agriculture and Forestry: Airborne LiDAR is used for agricultural landscape assessment, vegetation health monitoring, and forestry management. The system facilitates decision-making by offering comprehensive three-dimensional information on crop health, tree canopy structure, and terrain. Effective Disaster Response and Management: To swiftly evaluate and model impacted areas, airborne LiDAR is utilized in disaster response and management. Accurate mapping of changing topography, hazard detection, and emergency response activity planning are made possible by it. Applications for Surveying and Mapping: Airborne LiDAR is extensively used in surveying and mapping because it provides quick and accurate data collection for cadastral mapping, terrain modelling, and other geospatial applications. The accuracy and efficiency of surveying procedures are improved by technology. Developments in LiDAR Sensor Technology: The market for aerial LiDAR is expanding as a result of continuous developments in LiDAR sensor technology, which include the creation of lightweight, high-resolution sensors. More precise and comprehensive data collection is made possible by enhanced sensor capabilities. Demand for 3D Modelling and Visualization: The adoption of aerial LiDAR is propelled by the need for 3D modelling and visualization across a range of industries, such as environmental monitoring, urban planning, and geographic mapping. It makes it possible to create intricate 3D models of buildings and landscapes. Crop monitoring and Precision Agriculture: Airborne LiDAR technology is beneficial to agriculture in the context of precision agriculture applications. LiDAR data provides comprehensive information on terrain, vegetation, and soil conditions, which helps with crop monitoring, yield estimation, and farming practice optimization. Environmental Monitoring and Conservation: Efforts to monitor and conserve the environment make use of aerial LiDAR. By giving precise spatial data, it supports conservation efforts, helps evaluate changes in ecosystems, and monitors wildlife habitats. Demand for LiDAR in driverless cars: The advancement of advanced driver-assistance systems (ADAS) and driverless cars is one factor driving the need for LiDAR technology. For navigation and obstacle detection, precise and current maps can be made using airborne LiDAR data. Natural Resource Management: By offering comprehensive data on terrain, waterways, and forests, airborne LiDAR aids in the management of natural resources. Planning sustainably, analyzing wildlife habitats, and keeping an eye on the health of ecosystems can all benefit from this data. Government rules and laws: The use of aerial LiDAR technology is influenced by government rules and laws pertaining to mapping standards, geospatial data, and mapping. Adherence to guidelines established by governmental entities promotes the utilization of LiDAR in diverse contexts. Growing Need for LiDAR in Construction: Topographic surveys, terrain modelling, and construction site planning are all done by the construction sector using aerial LiDAR. LiDAR's precise and up-to-date data improves construction procedures and reduces problems.

Original Dataset Product: Processed, classified lidar point cloud data tiles in LAZ 1.4 format. Original Dataset Geographic Extent: HI_NOAAMauiOahu_3: The work unit covers approximately Approximately 306 square miles on the eastern side of the big island of Hawaii. Original Dataset Description: HI_NOAAMauiOahu_3 (Big Island) The HI_NOAAMauiOahu_3_B20 lidar project called for the planning, acquisition, processing, and production of derivative products of QL1 lidar data to be collected an aggregate nominal pulse spacing (ANPS) of 0.35-meters and 8 points per square meter (ppsm). Project specifications were based on the National Geospatial Program Lidar Base Specification Version 2.1, and the American Society of Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards for Digital Geospatial Data (Edition 1, Version 1.0). The data was developed based on a horizontal reference system of NAD83 (PA11), UTM 5 (EPSG 6635), Meter, and a vertical reference system of NAVD88 (GEOID12B), Meter. Lidar data was delivered as processed LAZ 1.4 files formatted to 3,450 individual 500-meters x 500-meters tiles. Note: Between 2020 and 2023 multiple mobilizations were made to collect the data in the project area due to the extreme terrain and persistent low clouds. On March 31, 2023, it was decided between Woolpert and USGS to end the acquisition phase of the project and move onto processing with the data collected. The DPA and work unit has been clipped to the extent of the data collected. Areas of low point density and/or small data voids within the work unit have been identified with low confidence polygons. Original Dataset Ground Conditions: HI_NOAAMauiOahu_3 (Big Island) Lidar was collected from February 14, 2023, through March 15, 2023 while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Woolpert established ground control points that were used to calibrate the lidar to known ground locations established throughout the entire project area. An additional independent accuracy checkpoints were collected throughout the entire project area and used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.