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

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 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 per hectare 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 per merchant on Predicted foot in others results of forest compilations, in the Tariff of cubing and prediction models 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 depending on 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).**

Lidar Technology in Mapping Market Outlook

The global Lidar Technology in Mapping market size was valued at approximately USD 1.6 billion in 2023 and is expected to reach USD 5.3 billion by 2032, growing at a robust CAGR of 14.2% during the forecast period. The major growth factors driving this market include the rapidly increasing demand for high-resolution topographic and spatial data, advancements in Lidar technology, and its expanding application across various industries such as transportation, urban planning, and forestry.

One of the primary growth factors for the Lidar Technology in Mapping market is the increasing demand for high-resolution maps and spatial data, which are essential for a wide range of applications including environmental monitoring, urban planning, and disaster management. High-resolution Lidar data enables precise mapping of terrain features and land cover, facilitating better decision-making processes. As urbanization continues to expand globally, the need for accurate and detailed maps has become more crucial, driving the growth of this market.

Technological advancements in Lidar systems have significantly contributed to the market's growth. Modern Lidar systems are now more compact, efficient, and capable of capturing data at higher resolutions than ever before. Innovations such as solid-state Lidar and the integration of Lidar with advanced imaging technologies like hyperspectral and multispectral cameras have enhanced the capabilities of Lidar systems. These advancements have broadened the scope of Lidar applications, making it an indispensable tool in various fields including forestry, archaeology, and civil engineering.

Another significant growth driver is the expanding application of Lidar technology in autonomous vehicles and drones. Lidar is a crucial component in the navigation systems of autonomous vehicles, providing accurate and real-time 3D mapping of the environment. Similarly, the use of drones equipped with Lidar sensors for aerial surveys and inspections has gained popularity, offering a cost-effective and efficient solution for mapping large and inaccessible areas. This growing adoption of Lidar technology in emerging applications is expected to further fuel the market's growth.

The regional outlook for the Lidar Technology in Mapping market shows significant growth potential across various regions, with North America and Asia Pacific leading the way. North America, particularly the United States, has been a major market due to the early adoption of advanced technologies and substantial investments in infrastructure development and environmental monitoring. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate, driven by rapid urbanization, infrastructure projects, and increasing government initiatives for smart city development. Europe also holds a substantial market share, supported by the presence of key market players and extensive research activities in Lidar technology.

Component Analysis

The Lidar Technology in Mapping market can be segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of Lidar systems. The hardware segment includes Lidar sensors, GPS receivers, IMUs, and other related components. The software segment comprises data processing and analysis tools that convert raw Lidar data into useful information. The services segment includes installation, maintenance, and consulting services that support the deployment and operation of Lidar systems.

In the hardware segment, Lidar sensors are the most critical component, responsible for emitting laser pulses and measuring the time it takes for them to return after hitting an object. Recent advancements in sensor technology have led to the development of more compact and efficient sensors, capable of capturing high-resolution data at longer ranges. These innovations have not only improved the accuracy and reliability of Lidar systems but also reduced their size and cost, making them more accessible for various applications.

The software segment is equally important, as it involves the processing and analysis of raw Lidar data to generate detailed maps and models. Advanced software tools offer features such as point cloud processing, terrain modeling, and feature extraction, enabling users to derive actiona

Defense LiDAR Mapping Market Outlook

According to our latest research, the global Defense LiDAR Mapping market size in 2024 stands at USD 2.31 billion, reflecting the sectorÂ’s robust integration into modern defense strategies. The market is experiencing a compound annual growth rate (CAGR) of 11.6% from 2025 to 2033. By the end of the forecast period in 2033, the Defense LiDAR Mapping market is expected to reach a value of USD 6.23 billion. This growth is primarily driven by increasing defense modernization initiatives, the rising need for precise geospatial intelligence, and the proliferation of unmanned systems across global defense forces.

A significant growth factor for the Defense LiDAR Mapping market is the escalating demand for high-resolution, real-time mapping solutions in military operations. Modern warfare increasingly relies on superior situational awareness, and LiDAR technology delivers unparalleled accuracy in terrain modeling, obstacle detection, and mission planning. The integration of LiDAR mapping into defense platforms enables rapid data acquisition and processing, which is crucial for time-sensitive operations such as surveillance, reconnaissance, and target acquisition. Furthermore, the growing complexity of military missions, including urban warfare and border security, necessitates advanced mapping capabilities, making LiDAR an indispensable tool for armed forces worldwide.

Another key driver is the rapid advancement in LiDAR hardware and software technologies. Innovations such as solid-state LiDAR sensors, multi-wavelength systems, and AI-powered data analytics are enhancing the range, resolution, and reliability of defense LiDAR mapping solutions. These technological improvements are not only reducing the size and weight of LiDAR systems, making them more suitable for deployment on a variety of platforms including UAVs and ground vehicles, but are also lowering operational costs. The increasing interoperability of LiDAR with other sensing technologies, such as radar and electro-optical systems, is further expanding its utility across diverse defense applications, from navigation to threat detection.

The rising investments in defense infrastructure and the expansion of military budgets, particularly in emerging economies, are also fueling market growth. Governments across North America, Europe, and Asia Pacific are prioritizing the adoption of advanced geospatial intelligence tools to enhance national security and operational effectiveness. The integration of LiDAR mapping into national defense strategies is being supported by public-private partnerships, research grants, and collaborative projects with technology firms. As a result, the Defense LiDAR Mapping market is witnessing increased procurement activities, pilot projects, and field deployments, setting the stage for sustained growth over the next decade.

Regionally, North America continues to dominate the Defense LiDAR Mapping market, accounting for the largest share in 2024. This leadership is attributed to the presence of major defense contractors, robust R&D capabilities, and substantial government funding for military modernization programs. Europe follows closely, driven by cross-border security initiatives and the adoption of advanced surveillance technologies by NATO allies. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rising geopolitical tensions, territorial disputes, and significant investments in defense technology by countries such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as these regions increasingly recognize the strategic value of LiDAR mapping in border control and counterterrorism operations.

The advancement of LiDAR technology has also paved the way for innovative applications like LiDAR Perimeter Detection. This application is particularly significant in enhancing security measures for military installations and critical infrastructure. By using LiDAR systems to establish a virtual perimeter, defense organizations can achieve real-time monitoring and threat detection. The precision and accuracy of LiDAR allow for the identification of unauthorized entries or breaches, thereby bolstering security protocols. This technology is especially beneficial in environments where traditional surveillance

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

Using LiDAR to identify structural and volcanic evolution of a Miocene-Pleistocene age bimodal volcanic complex and implications for geothermal potential. The file includes an updated geologic map, methods, and preliminary results.

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.

Construction Drone LiDAR Mapping Market Outlook

According to our latest research, the global Construction Drone LiDAR Mapping Market size in 2024 stands at USD 1.12 billion, demonstrating robust momentum driven by increasing adoption of advanced geospatial technologies in the construction sector. The market is experiencing a strong compound annual growth rate (CAGR) of 17.3%, and is forecasted to reach USD 4.17 billion by 2033. This significant expansion is propelled by the growing demand for high-precision mapping, faster project delivery, and enhanced site safety, making drone-based LiDAR solutions an indispensable asset for modern construction and infrastructure projects worldwide.

One of the primary growth factors for the Construction Drone LiDAR Mapping Market is the increasing need for accurate and efficient topographic data acquisition in large-scale construction and infrastructure projects. Traditional surveying methods are often time-consuming and labor-intensive, whereas drone-based LiDAR mapping provides rapid, high-resolution, and precise geospatial data. This technological advancement enables construction companies to streamline project planning, monitor progress in real-time, and reduce operational costs. The integration of LiDAR sensors with drones allows for comprehensive area coverage, even in challenging terrains, which is particularly beneficial for projects involving roads, bridges, tunnels, and urban development. The construction industry’s emphasis on digital transformation and the adoption of Building Information Modeling (BIM) further amplify the demand for advanced mapping solutions, positioning drone LiDAR mapping as a critical tool for next-generation construction workflows.

Another significant driver is the rising focus on safety and regulatory compliance within the construction sector. As construction sites become more complex and regulations more stringent, there is a heightened requirement for accurate documentation and regular site inspections. Drone LiDAR mapping minimizes the need for manual site visits, thereby reducing the risk of accidents and ensuring compliance with safety standards. The ability to capture detailed 3D models and monitor site changes in real-time enhances decision-making and facilitates proactive risk management. Additionally, the growing trend of smart cities and infrastructure modernization in both developed and emerging economies is fueling investments in drone-based geospatial technologies. Governments and municipalities are increasingly leveraging these solutions for urban planning, asset management, and disaster response, further expanding the market’s potential.

Technological innovations in drone hardware and LiDAR sensors are also propelling market growth. The development of lightweight, high-precision LiDAR systems and the integration of artificial intelligence (AI) and machine learning (ML) algorithms for data processing are transforming the capabilities of construction drone mapping. These advancements enable faster data acquisition, improved accuracy, and automated analysis, making it easier for stakeholders to extract actionable insights from complex datasets. The proliferation of cloud-based platforms for data storage and sharing further enhances collaboration among project teams, driving the adoption of drone LiDAR mapping across various construction applications. As the cost of drone technology continues to decrease and accessibility improves, even small and medium-sized construction firms are embracing these solutions to gain a competitive edge.

Regionally, North America currently leads the Construction Drone LiDAR Mapping Market, accounting for over 38% of the global market share in 2024, followed closely by Europe and Asia Pacific. The presence of major construction companies, advanced regulatory frameworks, and high adoption rates of digital construction technologies are key factors supporting market dominance in these regions. Meanwhile, the Asia Pacific region is expected to witness the fastest growth, with a projected CAGR of 19.1% from 2025 to 2033, driven by rapid urbanization, infrastructure development, and government initiatives promoting smart city projects. Latin America and the Middle East & Africa are also emerging as promising markets, supported by increasing investments in infrastructure and construction modernization.

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 ...

The Download Tool is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. The tool provides easy download access to aerial imagery and lidar elevation collected during multiple flights.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://res1coastd-o-tnoaad-o-tgov.vcapture.xyz/slr. This metadata record describes the Florida, SW digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Charlotte, Collier, Glades, Hendry, Miami-Dade, Monroe, and Palm Beach Counties. The DEM was produced from the following lidar data sets: 1. 2018 Florida Peninsular FDEM - Charlotte 2. 2018 Florida Peninsular - Collier 3. 2017 Everglades FL Lidar 4. 2018 West Everglades Topobathy NP FL Lidar 5. 2018 Southeast FL Lidar (B1, B2, TL) 6. 2018 Southwest FL Lidar (A, B, B TL) 7. 2018 Florida Peninsular FDEM - Glades 8. 2018 Florida Peninsular FDEM - Hendry 9. 2015 Miami-Dade County, Florida Lidar 10. 2017 Palm Beach County, Florida Lidar 11. 2014 Seminole Tribe Big Cypress Reservation Lidar The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

The 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 dataset contains maps of alluvial and hillslope gullies across blocks of lidar covering portions of the Burdekin Catchment. This project is an expansion of the detailed gully mapping and assessment undertaken previously as part of NESP TWQ Project 5.10 (Daley et al., 2021), using newly available lidar as well as some older data not previously used.

The gully polygons were generated using methods developed in the NESP TWQ 5.10 project for the extraction of gullies from lidar. Lidar is detailed topographic data collected from aircraft using an airborne laser scanning system.

Methods: Lidar mapping and Gully Assessment SON3352211, December 2023.

This project should be considered as the first step of a ‘prospecting’ effort, whereby high yielding high priority gullies are identified. Further investigations will be required (including on ground inspection) to firm up the prioritisation of gullies for rehabilitation. In all, gully mapping has been conducted across 1625 km2 (Figure 1). This is the area of lidar derived DEMs adjoining the areas within the Burdekin previously analysed by Daley et al. (2021) (Figure 1). This report briefly describes the generation of a new gully mapping dataset covering the 8 lidar blocks of the study area.

Daley et al. (2021) provide a comprehensive description and discussion of the methods used below. Two small departures from the methods outlined therein have been adopted here. Firstly, the production of data layers was reordered, such that analyses that were previously restricted to just areas mapped as eroded landforms (i.e. PAE and Bare Soil described below) were here undertaken across the whole landscape, with the presence of high values for these metrics being used to identify areas for further investigation. This is a reversal (in a sense) of the approach used by Daley et al, who mapped all “gully like” features and then used PAE and Bare Soil metrics to distinguish actual gullies from features merely gully like. Here the approach has been to only search for (and map) gullies within areas of high PAE and Bare Soil. The second difference adopted here has been to define gully boundaries using two separate techniques for all observed gullies. In Daley et al, the choice of technique used to define the gully boundary was based on interrogation of general landscape slope, with 2% selected as a threshold separating areas where the Multi-direction hillshade (MDHS) approach was used from areas where the Mean Digital Elevation Model of Difference (Mean DoD) method was used. Early experimentation as part of this project found that this threshold method was occasionally unsatisfactory, as there were instances across all slope classes where the alternative approach provided the better representation of gully outline. In general, it was found that the MDHS method worked best where the gully had a more open form, which generally, but not always, occurred in areas of lower slope. Likewise it was found that the Mean DoD method worked best for linear or more reticulated forms, which generally but not always occurred in areas of higher slope. Examples of where the later did not apply is when an open form gully has mostly stabilised and revegetated, then re-incised, with the early phases of this re-incision taking the form of inset, more or less linear and/or reticulated gullying. To avoid the large amount of manual editing required where an inappropriate method was applied, it was found to be more parsimonious to run both techniques across all gullies, then select the approach which provided the best definition of gully boundary, requiring the least amount of manual digitising.

1. Mosaiced Digital Elevation Models (DEM) One kilometre square DEMs were obtained from Geoscience Australia’s ELVIS portal (https://elevation.fsdf.org.au/) and mosaiced into 8 larger DEMs, each covering one of the 8 non-contiguous areas shown in Figure 1. The DEM data was used to define gully margins and derive the Potential Active Erosion (PAE) layers. As depicted in figure 1, the spatial resolution of the DEMs of blocks 1 to 5 is 0.5 m and blocks 6 to 8 is 1 m.

2. Potential Active Erosion (PAE) The PAE method developed (and wholly described) by Daley et al. 2021 is an index of landscape curvature or crenulation. The index uses a measure of surface roughness derived using a log-transformed standard deviation of terrain curvature. Most erosion activity indicators correspond to areas exhibiting high values of surface roughness, including fluting, rilling, block collapse, slumping and exposed tree roots. As erosion activity decreases, slopes relax to more diffuse forms with lower roughness. Terrain roughness was measured as the local standard deviation of curvature in a 3 m kernel window, assessed from total, plan and profile curvatures. As roughness was highly skewed, with most values approximating zero, the data was log-transformed for ease of interpretation.

Planform, profile and total curvatures were calculated within a 9-cell (3 x 3 m) neighbourhood using the ArcGIS curvature tool following the method of Moore et al. (1991) and Zevenbergen and Thorne (1987). All three types of curvature were evaluated to generate roughness indices following current literature as the log-normalised standard deviation of curvature (Korzeniowska et al. 2018; Patton et al. 2018). As standard deviation values in a 3 m cell kernel size were strongly right skewed, values were transformed using a base-10 logarithm function to normalise the distribution for ease of interpretation. Following Daley et al., a threshold log-normalised standard deviation of curvature value of 1.8 was chosen to define areas of PAE.

1. Bare Soil Baresoil was determined using PlanetScope Analytic data, with all scenes collected soon after the end of the 2022-2023 wet season. PlanetScope provides 4-band multi-spectral ortho scene data for analytic and visual applications. The provided product is orthorectified, radiometrically calibrated into top-of-atmosphere radiance data and then atmospherically corrected to surface reflectance, resampled to 3 m (Planet Team, 2020). This data was specifically selected for 90% coverage in a given acquisition. Additional data from neighbouring days were selected to fill in any gaps for complete coverage. Following data acquisition, scenes were mosaiced in a GIS to provide a continuous coverage dataset across the 8 study areas. Bare soil was derived from the PlanetScope imagery using the modified secondary soil-adjusted vegetation index (MSAVI2), Qi et al. (1994). MSAVI2 was selected as the most appropriate vegetation index for the region for its capacity to separate vegetation signatures in areas where vegetation cover is low, or features a high degree of exposed soil surface, and has the benefit over other soil-adjusted vegetation indices in that it does not require the calculation of a soil-brightness correction factor. MSAVI2 was developed iteratively by Qi et al. (1994) to determine the per-pixel difference of the red band reflectance value against the near infrared band, using the following equation:

MSAVI2 = (2×NIR + 1 - √((2×NIR+1)^2 - 8×(NIR-RED)))/2

This yields an output of vegetation greenness with values ranging from -1 to +1. Following Daley et al, a threshold of 0.35 was used to identify bare soils.

1. Gully Mapping Gully boundaries were produced using the aforementioned Multi-direction hillshade (MDHS) and Mean DEMoD automated geomorphic mapping algorithms. For MDHS, the hillshade tool in ArcGIS Spatial Analyst is used iteratively, with the sun angle set at 15 degrees and passed through the six azimuths (0, 60, 120, 180, 240, 300 degrees). The output rasters of the hillshades are then mosaiced and areas <= 55 (i.e., lowest greyscale intensity value) are selected. These values are generally a function of slope and aspect for any given cell, but in this case, they are viewed as the amount of shadow generated by the moving sun. At each azimuth, the boundary of an eroded land form (ELF) is shadowed perpendicular to the direction of light. By merging the six hillshades, the boundary of a given gully is accurately extracted (see cross section C in Figure 2).

In certain instances when a feature has low or diffuse walls, or a broad floor, the centre of the gully may not be shadowed, hence the centre of these gullies are filled to create a clean boundary.

The Mean DoD method identifies abrupt changes in elevation assuming such changes equate to steep slopes on the walls of gullies. The mean focal statistic function was used with a circular window having a kernel radius of 25m.

1. Gully Distribution The area covered by the gully mapping polygons was divided into a 1 km x 1 km grid. One or two square kilometres is assumed to represent the size of an area that could be managed as one site containing multiple gullies. For each polygon the total area of mapped gullies contained within has been calculated (Figure 4) and plotted as a heat map. This is to facilitate prioritisation based on combined gully area within close proximity, rather than based on the size of any single gully.

Limitations of the data: The funding for this project necessitated different priorities and a different scope of works than that of NESP TWQ 5.10, and consequently the set of derived gully metrics in this project is a subset of those in NESP TWQ 5.10. This dataset is specifically a selection of large prospectively high yielding gullies mapped as preparatory work towards future landscape repair programs which require detailed maps of gully extent in order to enable prioritisation and planning of works.

This project should be considered as the first step of a ‘prospecting’ effort, whereby high yielding high priority gullies are identified. Further investigations will be required (including on ground inspection) to firm up the prioritisation of gullies for rehabilitation.

Format of the data: Two shapefiles The

Manhole Lidar Mapping Robot Market Outlook

According to our latest research, the global Manhole Lidar Mapping Robot market size reached USD 418.2 million in 2024, driven by rapid advancements in robotics and Lidar technology, as well as increasing demand for efficient underground infrastructure inspection. The market is projected to grow at a robust CAGR of 15.7% from 2025 to 2033, reaching a forecasted value of USD 1,355.4 million by 2033. This impressive growth is underpinned by the urgent need for smart city solutions, infrastructure modernization, and heightened safety standards for municipal and utility networks globally.

The primary growth factor for the Manhole Lidar Mapping Robot market is the surging global investment in urban infrastructure and smart city initiatives. Urbanization is accelerating at an unprecedented pace, especially in emerging economies, leading to increased pressure on aging underground utility networks, sewer systems, and drainage infrastructure. Traditional inspection methods are labor-intensive, costly, and often expose workers to hazardous environments. The adoption of advanced Lidar mapping robots not only enhances the accuracy and speed of inspections but also significantly reduces operational risks and costs. These robots are equipped with cutting-edge Lidar sensors and autonomous navigation systems, enabling municipalities and utility companies to carry out comprehensive inspections with minimal human intervention, thus ensuring the longevity and safety of critical infrastructure assets.

Another significant driver of market expansion is the ongoing evolution of Lidar technology itself. The transition from 2D to 3D Lidar sensors, coupled with the integration of artificial intelligence and machine learning algorithms, has revolutionized the capabilities of manhole mapping robots. These technological advancements have enabled robots to capture high-resolution, three-dimensional visualizations of underground spaces, facilitating precise defect detection, structural assessments, and predictive maintenance. The growing preference for data-driven asset management among municipal authorities and private utility operators is fueling demand for these advanced Lidar mapping solutions. Furthermore, the increasing availability of hybrid systems that combine multiple sensor modalities is broadening the application scope of these robots, making them indispensable tools for modern infrastructure management.

Moreover, stringent regulatory mandates and safety standards are compelling stakeholders to adopt innovative inspection technologies. Regulatory bodies across North America, Europe, and Asia Pacific are enforcing stricter guidelines for the maintenance and monitoring of underground utilities, aiming to prevent accidents, environmental contamination, and service disruptions. Manhole Lidar Mapping Robots provide a compliant, efficient, and non-invasive solution for meeting these regulatory requirements. Additionally, the rising awareness about environmental sustainability and the need to minimize urban disruptions during inspection activities are further catalyzing market growth. These factors, combined with ongoing R&D investments and partnerships between technology providers and government agencies, are expected to sustain the momentum of the market in the coming years.

Regionally, North America and Europe are currently leading the market, driven by mature infrastructure networks, high adoption rates of automation, and proactive regulatory frameworks. However, the Asia Pacific region is poised for the fastest growth, supported by rapid urbanization, significant infrastructure development projects, and increasing government focus on smart city technologies. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a more gradual pace, as awareness and investment in advanced inspection technologies continue to grow. Overall, the global Manhole Lidar Mapping Robot market is set to experience robust expansion, with technology innovation and infrastructure modernization serving as the primary catalysts.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. Products derived from lidar (Light Detection and Ranging) are generated as part of the provincial lidar sensor data acquisition project. It is therefore to facilitate the use of raw lidar data and optimize its benefits that the Ministry of Natural Resources and Forests (MRNF) generated and made available products derived from lidar in a user-friendly format. Lidar technology makes it possible to accurately provide information such as ground altitude, forest cover height (canopy), slopes, and contour lines. Here is the list of the five derived products: + Digital terrain model (spatial resolution: 1 m) + Digital terrain model in shaded relief (spatial resolution: 2 m) + Canopy height model (spatial resolution: 1 m) + Slopes (spatial resolution: 2 m) + Slopes (spatial resolution: 2 m) + Level curve (interval of: 1 m) This data covers almost the entire territory of Quebec south of the 52nd parallel. This map is distributed by map sheet at a scale of 1/20,000. _ ⚠️ 1) Note that_ the resolution of the following products (digital terrain model, digital terrain model in shaded relief, canopy height model and slopes) has been slightly degraded when viewed in the interactive map to ensure efficient display. _ ⚠️ 2) Note that_ the planimetric and altimeter accuracy of curves is variable, but inevitably lower than that of the lidar surveys used to generate them. Moreover, it is recommended to use these level curves only for visual representations, and not for quantitative analyses.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

U.S. Geological Survey (USGS) staff created geographic information system (GIS) footprints to show the extent of light detection and ranging (lidar) datasets published by the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC), since 2001. These lidar datasets were published as LAS, XYZ, or Digital Elevation Model (DEM) outputs of coastal, submerged and/or terrestrial topography in USGS Data Series (DS), Open-File Reports (OFR), and data releases (DR). Please see the publications listed in the source information section of this metadata record for details on data acquisition and processing of the datasets included in this data release. Using tools included in Global Mapper (GM) GIS software, polygons were generated to represent the coverage area of data provided in multiple USGS lidar publications. These footprints were later merged into one shapefile containing information about the field activity number (fan), field activity source link (fan_url; added in version 2.0), publication type (pub), publication source link (pub_url), lidar return type (returntype), and year the data were collected (yr_collect) to serve as an easily accessible data inventory. This data release will be updated and versioned, as needed, as more lidar publications are released from the USGS SPCMSC.

The link: Access the data directory is available in the sectionDataset 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 map and data are modeled inundation time using LEAN (Lidar Elevation Adjustment with NDVI) corrected lidar data. These data are used to support the project and products described below. Tidal marshes support coastal food webs, improve water quality, and buffer against storm and wave damage. Sea level influences the structure and function of coastal marshes in ways that alter the services they provide. This project enhanced a tidal marsh model with new field data to better understand the impacts of sea-level rise on marshes in the San Francisco Bay-Delta Estuary, allowing coastal managers to evaluate wetland vulnerability and inform restoration. Results of this project provide managers in California and other regions with improved management tools, such as vegetation-corrected, digital elevation models (DEMs), and habitat predictions under sea-level rise.This application (https://storymaps.arcgis.com/stories/768622e923024ef19a211b5073af0e2b) highlights the outcomes and data products associated with our project, including the following:Improved estuary-wide data on vegetation, productivity, and decomposition responses of tidal marsh plant species under various elevation and salinity gradients.Refined marsh elevations using remotely sensed and on-the-ground GPS data, resulting in a high-resolution estuary-wide digital elevation model.Documented sediment deposition rates based on plant species composition, season, storms, and tidal elevation, improving parameters required for sea-level rise models.Projections of future habitat distributions from WARMER (Wetland Accretion Rate Model of Ecosystem Resilience) using the updated biological and physical processes parameters to assess marsh accretion.This project was led by Oregon State University and the U.S. Geological Survey and was funded through NOAA’s Effects of Sea Level Rise program. Additional details on the project and links to publications associated with this project are available here: https://www.usgs.gov/centers/werc/science/coastal-ecosystem-response-sea-level-rise?qt-science_center_objects=0#qt-science_center_objectshttps://coastalscience.noaa.gov/project/ecosystem-model-inputs-sea-level-rise-vulnerability-san-francisco-bay-estuary/

The global underground utility mapping software market is experiencing robust growth, driven by increasing urbanization, the expansion of infrastructure projects, and stringent regulations emphasizing the prevention of damage to underground utilities. The market's value, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of approximately 12% from 2025 to 2033, reaching an estimated market size of $7 billion by 2033. Key drivers include the rising adoption of 3D modeling and GIS technologies for improved visualization and planning, coupled with the increasing demand for accurate and efficient utility mapping to minimize excavation risks and associated costs. Furthermore, the integration of advanced technologies like LiDAR, GPR, and sensor fusion is enhancing the accuracy and speed of data acquisition, leading to wider adoption across various sectors, including construction, telecommunications, and energy. Market growth is also fueled by the increasing availability of cloud-based solutions that offer scalability, cost-effectiveness, and accessibility. However, factors such as high initial investment costs for software and hardware, the need for skilled professionals to operate the systems, and data security concerns act as market restraints. Segmentation reveals a strong preference for solutions providing comprehensive functionalities including data management, analysis, and collaboration tools, catering to various user needs across different organizational sizes. Key players like ProStar, Juniper Systems, 4M Analytics, Geolantis, Hexagon, Sensors & Software, AEC Solutions, Bentley, Radar Systems, and Geoinfo are actively shaping the market landscape through continuous innovation and strategic partnerships. The market is expected to see continued consolidation and the emergence of innovative solutions focusing on automation and artificial intelligence in the coming years.