These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using a Riegl VQ820G system. The data were acquired from 20140108 - 20140522 in four missions. The missions flown on 20140108 and 20140109 represent Low Water missions and the missions flown on 20140516 and 20140522 represent High Water (everything outside of MLLW tidal requirements) missions. The data includes topobathy data in an LAS 1.2 format file classified as unclassified (1), ground (2), topo noise (7), refracted High Water data landward of the MLLW land/water interface (18), bathy noise (22), noise as defined by the sensor (23), refracted sensor noise (24), water column (25), bathymetric bottom or submerged topography (26), water surface (27), International Hydrographic Organization (IHO) S-57 objects (30), and temporal bathy bottom (31) in accordance with project specifications. Several of the noise classes were filtered out prior to distribution on the Digital Coast. The full project consists of 2,775 square miles along the Atlantic Coast from New York to South Carolina. This dataset represents a contiguous area covering a portion of acquisition block 1 to 140 with 500 m x 500 m lidar tiles. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division Title: Chief, Remote Sensing Division Phone: 301-713-2663

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

Web map displaying Wisconsin DNR-produced Digital Elevation Model (DEM) and Hillshade image services, along with their index layer, in formats that are clickable and can be symbolized and filtered. This map can also be used as a starting point to create a new map. To open the web map from DNR's GIS Open Data Portal, click the View Metadata: link to the right of the description, then click Open in Map Viewer.

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).

Used by Districts to show the difference between the LiDAR collected clearance and what we had on record.

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

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...

A web layer used for sharing LiDAR data in the Jackson County Open Data Hub site.

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.

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. The LiDAR dendrometric map presents various dendrometric characteristics that are 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 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 for certain species groups • Average usable volume per stem and average diameter for certain species groups • Average usable volume per stem and average diameter for certain species groups The volumes compiled in the LiDAR dendrometric map are variables distinct from the gross volume market on Predicted foot in others results of forest compilations, in the Cubage Tariff and for the stems counted in the sample plots of the ecoforestry 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 based on the LiDAR acquisition. Note: 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).**

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.

Robots, such as unmanned aerial vehicles and robot dogs, are suitable for exploring unknown environments that are dangerous for a human to walk in. These robots are equipped with sensors to obtain information from the environment. A map of the environment is simultaneously constructed on the robot while an online exploration algorithm determines the next area to explore based on the map. In most recent research on robotics, LiDARs have gained increasing interest thanks to their ability to provide dense and accurate depth measurements of environments. This dataset includes LiDAR data collected in both indoor and outdoor environments of HKU, which aims to benefit the robotic community and facilitate future research to advance the LiDAR mapping technology.

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 in shapefile format. 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 may vary by project. Some project 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 Documentation

Ontario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX)

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)

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) GTA 2023 - Lift Metadata (SHP)

Ontario Classified Point Cloud (Lidar-Derived) - Tile Index (SHP)

Ontario Lidar Project Extents (SHP)

Data Package Sizes

LEAP 2009 - 22.9 GB

OMAFRA Lidar 2016-18 - Cochrane - 442 GB OMAFRA Lidar 2016-18 - Lake Erie - 1.22 TB OMAFRA Lidar 2016-18 - Peterborough - 443 GB

GTA 2014 - 57.6 GB GTA 2015 - 63.4 GB Brampton 2015 - 5.9 GB Peel 2016 - 49.2 GB Milton 2017 - 15.3 GB Halton 2018 - 73 GB

CLOCA 2018 - 36.2 GB

South Nation 2018-19 - 72.4 GB

York Region-Lake Simcoe Watershed 2019 - 75 GB

Ottawa River 2019-20 - 836 GB

Lake Nipissing 2020 - 700 GB

Ottawa-Gatineau 2019-20 - 551 GB

Hamilton-Niagara 2021 - 660 GB

OMAFRA Lidar 2022 - Lake Huron - 204 GB OMAFRA Lidar 2022 - Lake Simcoe - 154 GB

Belleville 2022 - 1.09 TB

Eastern Ontario 2021-22 - 1.5 TB

Huron Shores 2021 - 35.5 GB

Muskoka 2018 - 72.1 GB Muskoka 2021 - 74.2 GB Muskoka 2023 - 532 GB The Muskoka lidar projects are available in the CGVD2013 or CGVD28 vertical datums. Please specifify which datum is needed when ordering data.

Digital Elevation Data to Support Flood Mapping 2022-26:

Huron-Georgian Bay 2022 - 1.37 TB Huron-Georgian Bay 2023 - 257 GB Huron-Georgian Bay 2023 Bruce - 95.2 GB Kawartha Lakes 2023 - 385 GB Sault Ste Marie 2023-24 - 1.15 TB Sudbury 2023-24 - 741 GB Thunder Bay 2023-24 - 654 GB Timmins 2024 - 318 GB

GTA 2023 - 985 GB

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

LiDAR in Mapping Market Outlook

The global LiDAR in Mapping market size is projected to grow from USD 1.2 billion in 2023 to USD 3.8 billion by 2032, reflecting a CAGR of 13.3%. This significant growth is driven by the increasing demand for precise mapping and surveying solutions across various industries. The adoption of LiDAR technology is bolstered by the rapid advancements in sensor technology, the growing need for high-resolution topographic data, and the expanding applications of LiDAR in urban planning, environmental monitoring, and infrastructure development.

The LiDAR technology's remarkable growth is largely due to its unparalleled ability to produce high-resolution, three-dimensional images of the Earth's surface. This capability makes it an indispensable tool in urban planning, where detailed and accurate mapping is crucial for efficient development and management. The rising urbanization and the need for smart city planning are significant factors contributing to the market's expansion. Moreover, the growing awareness about the environmental impact of urban sprawl has led to an increased demand for LiDAR in environmental monitoring and disaster management.

Another crucial growth driver for the LiDAR in Mapping market is the increasing investment in infrastructure development worldwide. Governments and private sector stakeholders are increasingly utilizing LiDAR technology to ensure precision and efficiency in construction projects. This technology not only enhances the accuracy of topographic surveys but also reduces the time and cost associated with traditional surveying methods. As a result, the construction and transportation sectors are witnessing a surge in the adoption of LiDAR solutions.

Furthermore, the integration of LiDAR technology with other advanced technologies such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) is opening new avenues for market growth. These integrations enhance the capabilities of LiDAR systems, making them more versatile and efficient. For instance, the combination of LiDAR with AI enables real-time data processing and analysis, which is particularly useful in disaster management scenarios. This technological synergy is expected to drive the market's growth throughout the forecast period.

Regionally, North America dominates the LiDAR in Mapping market due to the early adoption of advanced technologies and significant government investments in infrastructure projects. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid urbanization, increasing infrastructure development, and supportive government initiatives in countries like China and India are key factors driving the market growth in this region.

Component Analysis

In the LiDAR in Mapping market, components are broadly categorized into Hardware, Software, and Services. Each of these components plays a crucial role in the overall functionality and efficiency of LiDAR systems. Hardware comprises the LiDAR sensors and other physical components necessary for data collection. The continuous advancements in sensor technology, such as the development of compact and lightweight LiDAR sensors, are driving the growth of this segment. These innovations are making it easier to deploy LiDAR systems in various applications, from terrestrial to airborne mapping.

Software is another critical component in the LiDAR in Mapping market as it is responsible for data processing, analysis, and visualization. The increasing complexity of data collected by LiDAR sensors necessitates sophisticated software solutions capable of handling large datasets efficiently. Advances in software algorithms and the incorporation of AI and machine learning techniques are enhancing the capabilities of LiDAR software, making it more efficient in producing accurate and high-resolution maps. This segment is expected to witness significant growth as software solutions become more advanced and user-friendly.

The Services segment includes various support and maintenance services provided by companies to ensure the optimal functioning of LiDAR systems. These services are essential for the seamless operation of LiDAR technology, as they offer regular updates, troubleshooting, and training for users. The growing adoption of LiDAR technology across different industries is driving the demand for specialized services that can cater to the specific needs of various applications. This, in turn, is contributing to the overall growth of the Services segment.

Aerial Lidar Mapping Solutions Market Outlook

The global aerial LiDAR mapping solutions market size was valued at approximately USD 1.25 billion in 2023 and is projected to grow to USD 4.50 billion by 2032, at a compound annual growth rate (CAGR) of 15.2%. This robust growth trajectory is primarily driven by the increasing demand for precise geospatial data and advancements in technology that have made LiDAR systems more efficient and affordable. Another significant growth factor is the expanding application of LiDAR technology across various industries, from forestry and agriculture to infrastructure and environmental monitoring, as these sectors seek more accurate and efficient mapping solutions.

One of the primary growth drivers for the aerial LiDAR mapping solutions market is the rising adoption of advanced technologies such as drones and unmanned aerial vehicles (UAVs). These platforms enable rapid and flexible data collection over large areas, significantly reducing the time and cost associated with traditional mapping methods. Additionally, the integration of artificial intelligence (AI) and machine learning algorithms into LiDAR systems enhances data processing capabilities, allowing for more accurate and comprehensive analysis. This technological evolution is making LiDAR increasingly indispensable for sectors that require high-precision mapping and data collection.

Another compelling factor contributing to market growth is the increasing need for disaster management and environmental monitoring. Governments and organizations worldwide are recognizing the importance of real-time data for effective disaster response and environmental conservation efforts. LiDAR technology provides detailed topographical information that is crucial for predicting natural disasters like floods and landslides, as well as for monitoring changes in forests, water bodies, and urban areas. This growing awareness and adoption in environmental applications are expected to drive significant demand for aerial LiDAR mapping solutions.

Furthermore, the burgeoning infrastructure sector is set to propel market growth substantially. As urbanization accelerates globally, there is a heightened need for detailed and accurate mapping of infrastructure projects. LiDAR technologies are increasingly being utilized to survey and map construction sites, roadways, and rail systems with unmatched precision, enabling better planning and execution. This is especially critical in smart city initiatives where accurate geospatial data is fundamental for planning efficient infrastructure and public services. Thus, the infrastructure boom is likely to be a key driver for the aerial LiDAR mapping solutions market through 2032.

The integration of UAV Surveying Laser Lidar technology is transforming the landscape of aerial mapping by enhancing the precision and efficiency of data collection. UAVs equipped with laser lidar systems can capture high-resolution topographical data with remarkable accuracy, even in challenging terrains. This capability is particularly beneficial for applications in forestry, agriculture, and infrastructure, where detailed spatial information is crucial. The use of UAVs for surveying not only reduces operational costs but also minimizes the environmental impact compared to traditional methods. As industries continue to recognize the advantages of UAV Surveying Laser Lidar, its adoption is expected to grow, further driving the demand for advanced aerial mapping solutions.

Regionally, North America is expected to dominate the aerial LiDAR mapping solutions market over the forecast period. This can be attributed to the region's early adoption of advanced technologies, substantial investments in disaster management and environmental monitoring, and the presence of key market players. Additionally, the Asia Pacific region is anticipated to exhibit the highest growth rate, driven by rapid urbanization, infrastructural developments, and increasing governmental initiatives to adopt advanced geospatial technologies. Europe and Latin America are also expected to witness substantial growth, spurred by the rising use of LiDAR in forestry, agriculture, and urban planning initiatives.

Component Analysis

The aerial LiDAR mapping solutions market is segmented into hardware, software, and services. The hardware segment encompasses LiDAR sensors, GPS units, and inertial measurement units (IMUs), which are essential for capturing accurate geospatial data. Advances in sensor

Lidar Mapping System Market Outlook

The global LiDAR mapping system market size was valued at approximately $2.1 billion in 2023 and is projected to reach $6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.6% during the forecast period. The market's robust growth is primarily driven by advancements in sensor technology, increasing demand for 3D imaging, and applications in various sectors such as automotive, aerospace & defense, and infrastructure development.

A significant growth factor for the LiDAR mapping system market is the increasing use of autonomous vehicles. LiDAR technology is essential for the development of self-driving cars, as it provides high-resolution 3D maps of the environment, enabling vehicles to navigate safely and efficiently. Major automotive companies and tech giants are heavily investing in LiDAR technology to enhance the capabilities of their autonomous driving systems, which in turn is propelling the demand for LiDAR mapping systems.

Another key driver is the rising demand for high-precision mapping and surveying in the construction and infrastructure sectors. LiDAR systems provide detailed 3D models of the terrain, which are crucial for planning and executing construction projects. The growing trend of smart cities and urbanization has further accelerated the adoption of LiDAR technology for infrastructure planning, monitoring, and management. Moreover, government initiatives and funding for technological advancements in geospatial solutions are providing a significant boost to the market.

The LiDAR mapping system market is also experiencing growth due to its applications in environmental monitoring and agriculture. LiDAR technology is being increasingly used for monitoring forests, managing natural resources, and assessing environmental changes. In agriculture, LiDAR is utilized for precision farming, helping farmers optimize crop yields and manage fields more effectively. The ability to provide accurate topographical data and analyze vegetation health is driving the adoption of LiDAR in these sectors.

LiDAR, which stands for Light Detection and Ranging, is a remote sensing technology that uses laser pulses to measure distances and create detailed, high-resolution maps of the environment. This technology is not only pivotal in the development of autonomous vehicles but also plays a crucial role in various other applications. For instance, in the field of archaeology, LiDAR is used to uncover hidden structures beneath dense forest canopies, providing insights into ancient civilizations. In the realm of meteorology, LiDAR systems are employed to study atmospheric phenomena, offering valuable data for weather prediction and climate research. The versatility and precision of LiDAR technology continue to expand its applications across diverse fields.

From a regional perspective, North America holds the largest market share for LiDAR mapping systems, driven by the presence of major technology companies and high investment in research and development. The Asia Pacific region is expected to witness the highest growth rate, attributed to rapid industrialization, infrastructure development, and increasing adoption of advanced technologies in countries such as China, India, and Japan. Europe also represents a significant market, supported by advancements in automotive technology and strong emphasis on environmental monitoring.

Component Analysis

The LiDAR mapping system market by component can be segmented into hardware, software, and services. The hardware segment includes the LiDAR sensors, GPS, IMU (Inertial Measurement Unit), and other vital components that form the backbone of LiDAR systems. This segment holds a substantial market share due to the necessity of these components in capturing high-resolution data. Continuous advancements in sensor technology, such as the development of solid-state LiDAR, are expected to drive the growth of the hardware segment.

The software segment encompasses the data processing and analytics software required to interpret the raw data collected by LiDAR sensors. This segment is witnessing significant growth as the demand for sophisticated data analysis tools increases. Software solutions are essential for converting the vast amounts of data collected into useful, actionable insights. Advances in machine learning and artificial intelligence are enhancing the capabilities of LiDAR software, making it more efficient and effective in

The data sets are generated using the OPTECH ALTM 70 kHz LIDAR system mounted onboard AeroMap's twin-engine Cessna 320 aircraft. Classified data sets such as this one may have varying posting due to some LIDAR pulses not reaching the ground caused by data anomalies. Accuracy statements are based on areas of moderate terrain. Diminished accuracies are to be expected in areas of extreme terrain and dense vegetation. The accuracy of each point is expected to meet the vertical accuracy standard; however, derived products may be less accurate in extreme terrain and dense vegetation due to a lesser number of points defining the bare-earth in these areas. The data were QA/QC'ed but some data holidays still exist. This data represents the last return data only. Original contact information: Contact Org: NOAA Office for Coastal Management Phone: 843-740-1202 Email: coastal.info@noaa.gov

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 Endpoints

https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer https://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DSM_LidarDerived/ImageServer (Government of Ontario Internal Users)

Additional Documentation

Ontario DSM (Lidar-Derived) - User Guide (DOCX)

OMAFRA Lidar 2016-2018 -Cochrane-Additional Contractor Metadata (PDF) OMAFRA Lidar 2016-2018 -Peterborough-AdditionalContractorMetadata (PDF) OMAFRA Lidar 2016-2018 -Lake Erie-AdditionalContractorMetadata (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)

Ontario DSM (Lidar-Derived) - Tile Index (SHP) Ontario Lidar Project Extents (SHP)

Product Packages Download links for the Ontario DSM (Lidar-Derived) (Word) Projects:

LEAP 2009 GTA 2014-18 OMAFRA 2016-18 CLOCA 2018 South Nation CA 2018-19 Muskoka 2018-23 York-Lake Simcoe 2019 Ottawa River 2019-20 Ottawa-Gatineau 2019-20 Lake Nipissing 2020 Hamilton-Niagara 2021 Huron Shores 2021 Eastern Ontario 2021-22 OMAFRA Lake Huron 2022 OMAFRA Lake Simcoe 2022 Belleville 2022 Digital Elevation Data to Support Flood Mapping 2022-26 Huron-Georgian Bay 2022-23 Kawartha Lakes 2023 Sault Ste Marie 2023-24 Sudbury 2023-24 Thunder Bay 2023-24 Timmins 2024

Greater Toronto Area Lidar 2023

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ontario Ministry of Natural Resources - Geospatial Ontario,geospatial@ontario.ca

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.DEFRA Data Services Platform Metadata URLDefra Network WMS server provided by the Environment Agency

The LiDAR in Mapping market is experiencing robust growth, driven by increasing demand for high-precision mapping solutions across various sectors. The market, estimated at $2.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The automotive industry's push for autonomous vehicles necessitates detailed and accurate 3D maps, boosting the adoption of LiDAR technology. Similarly, the infrastructure development and construction sectors are leveraging LiDAR for precise surveying and modeling, leading to increased efficiency and cost savings. Furthermore, advancements in LiDAR sensor technology, such as the development of smaller, lighter, and more cost-effective sensors, are expanding its applicability across diverse mapping applications. The increasing availability of high-performance computing and data processing capabilities further enhances the value proposition of LiDAR in mapping. The market is segmented by LiDAR type (solid-state and mechanical) and application (mobile mapping, aerial mapping, and others). Solid-state LiDAR is gaining traction due to its improved reliability and reduced maintenance costs. Mobile mapping currently holds the largest market share, with aerial mapping experiencing steady growth driven by advancements in drone technology and the rising adoption of UAVs. The geographical distribution of the LiDAR in Mapping market is spread across various regions, with North America and Europe currently leading the market due to established infrastructure and high technology adoption rates. However, the Asia-Pacific region is poised for significant growth, propelled by rapid urbanization and increasing investments in infrastructure projects. Key players in the LiDAR in Mapping market are continually innovating to enhance sensor capabilities and develop advanced data processing algorithms. This intense competition drives market growth and fuels the development of sophisticated mapping solutions. The challenges include high initial investment costs for LiDAR systems and the need for skilled professionals for data processing and interpretation, which can potentially hinder adoption in certain regions. Nevertheless, the overall market outlook for LiDAR in Mapping remains exceptionally positive, promising substantial growth and transformative applications across diverse industries in the coming years. This report provides an in-depth analysis of the burgeoning LiDAR in mapping market, projected to reach $25 billion by 2030. We delve into key trends, regional dominance, competitive landscapes, and future growth projections, offering invaluable insights for investors, industry players, and researchers. This analysis covers the global market, focusing on technological advancements, regulatory impacts, and emerging applications across various sectors. Search terms include: LiDAR mapping, 3D mapping, point cloud data, autonomous vehicles, aerial LiDAR, mobile LiDAR, solid-state LiDAR, LiDAR sensor, GIS data, surveying.