We developed an algorithm that was designed to create a spatial database of a forested transportation network using aerial LiDAR. The algorithm uses two main attributes, LiDAR intensity values and ground return density. The road extraction process was developed using aerial LiDAR from McDonald-Dunn Research Forest near Corvallis, Oregon, U.S.A. The road extraction process requires X, Y, Z coordinates, intensity values, canopy type, and the maximum road grade. To compare the results of the process, nine road segments were field surveyed with terrestrial LiDAR. The result of the road extraction process resulted in 80% true positives, 34% false positives, 20% false negatives, and 38% true negatives in identifying forest roads. The average absolute value difference in the road width between the two data sets were 1.1m, while the cut/fill slope differences were minimal (> 4%) and the difference in road cross slope was two percent. These results were comparable with other published studies that examined differences between LiDAR measurements and field measurements.

Raw project data is available by contacting ctemps@unr.edu

The lateral blast, debris avalanche, and lahars of the May 18th, 1980, eruption of Mount St. Helens, Washington, dramatically altered the surrounding landscape. Lava domes were extruded during the subsequent eruptive periods of 1980-1986 and 2004-2008. During 2022, U.S. Army Corps of Engineers contracted the acquisitions of airborne lidar surveys of Mount St. Helens crater and two primary drainages–upper North Fork Toutle River and South Fork Toutle River with GeoTerra, Inc. The U.S. Geological Survey generated a terrain dataset from the classified point cloud with supplied breaklines and modified lake hydro-flattening, then exported a single digital elevation model (DEM) of the ground surface (that is, 'bare earth'), including beneath forest cover. This USGS data release contains digital elevation and shaded relief data as 3-foot resolution raster datasets (2022dem.tif and 2022demhs.tif, respectively). This DEM can support a variety of earth science investigations.

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

The lateral blast, debris avalanche, and lahars of the May 18th, 1980, eruption of Mount St. Helens, Washington, dramatically altered the surrounding landscape. Lava domes were extruded during the subsequent eruptive periods of 1980-1986 and 2004-2008. Nearly four decades after the emplacement of the 1980 debris avalanche, high sediment production persists in the North Fork Toutle River basin, which drains the northern flank of the volcano. This high sediment production poses a risk of flooding to downstream communities along the Toutle and Cowlitz Rivers and of clogging the shipping channel of the Columbia River. Consequently, U.S. Army Corps of Engineers (USACE), under the direction of Congress, built a sediment retention structure on the North Fork Toutle River in 1989 to maintain an authorized level of flood protection. During 2006, 2010, 2011, 2012, 2013, and 2014, USACE contracted the acquisitions of six high-precision airborne lidar surveys of upper North Fork Toutle River valley near Mount St. Helens. All surveys used near infrared lasers except the 2014 topobathymetric lidar survey which used a green laser scanner. The U.S. Geological Survey (USGS) used classified returns and breaklines from these surveys to produce digital elevation models (DEMs) of the ground surface for each dataset, including beneath forest cover and shallow water surfaces. This USGS data release contains digital elevation data as a 3-foot resolution raster datasets (.tif files). This DEM can be used to develop sediment budgets and models of sediment erosion, transport, and deposition.

La Soufrière Volcano is a 1,220 m high stratovolcano that occupies the northern half of the island of St. Vincent, Lesser Antilles, Eastern Caribbean. It has a long history of explosive and sometimes devastating eruptions. Beginning in December 2020 and ending in April 2021, La Soufrière Volcano produced a Volcano Explosivity Index (VEI) 4 eruption that greatly impacted the landscape, communities, and infrastructure on the island of St. Vincent. The eruption produced intense ash plumes, heavy ashfall, and pyroclastic flows down several river valleys. During and following the eruption, destructive lahars (volcanic mudflows) impacted rivers valleys and coastal communities for months. The USGS-USAID Volcano Disaster Assistance Program (VDAP) provided remote assistance to our partners at the University of the West Indies Seismic Research Center (UWI-SRC) throughout the eruption, predominantly through satellite remote sensing observations. Following the eruption, VDAP contracted the ac ...

description: Under this task order, Precision Aerial Reconnaissance, LLC was contracted to provide acquisition and processing of airborne Lidar over St. Charles Parish, Louisiana. Airborne Lidar was captured to provide digital elevation models, lidar intensity imagery and contours for the project area. The NOAA Office for Coastal Management received the data for processing to the Digital Coast. This metadata record describes the 2 ft bare earth raster digital elevation models (DEMs) created from the lidar point data. In addition to the raster DEM data, the lidar point data are also available. These data are available for download here: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=6351 The DEM products have not been reviewed by the NOAA Office for Coastal Management (OCM) and any conclusions drawn from the analysis of this information are not the responsibility of NOAA, OCM or its partners.; abstract: Under this task order, Precision Aerial Reconnaissance, LLC was contracted to provide acquisition and processing of airborne Lidar over St. Charles Parish, Louisiana. Airborne Lidar was captured to provide digital elevation models, lidar intensity imagery and contours for the project area. The NOAA Office for Coastal Management received the data for processing to the Digital Coast. This metadata record describes the 2 ft bare earth raster digital elevation models (DEMs) created from the lidar point data. In addition to the raster DEM data, the lidar point data are also available. These data are available for download here: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=6351 The DEM products have not been reviewed by the NOAA Office for Coastal Management (OCM) and any conclusions drawn from the analysis of this information are not the responsibility of NOAA, OCM or its partners.

This data set contains topographic and bathymetric lidar data that were collected on April 21, 23, 30, May 2, and June 14, 17 of 2003, cooperatively by the U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and National Park Service (NPS). The data is for part of the U.S. Virgin Islands (island of St. John and a portion of the northern coastline of St. Croix)....

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

The aerial laser scanning (ALS) dataset, acquired on 7 April 2009, covers two seperate areas along the track leading to St Elie in French Guiana. The northern plot has an area of 4.5 sq.km. This area covers the 100 hectares IRD-biodiversity plot. The southern area (1.4 sq.km) covers a grove of Spirotropis longifolia. The Lidar data was acquired as part of the Guyafor project. It was shared with the ESA Tropisar project and the Biomass project at Jet Propulsion Laboratory (JPL).

description: The Light Detection and Ranging (LiDAR) dataset is a survey of the FY12 St Johns River Water Management LiDAR Survey, project area in north-central Florida and encompasses 800 square miles. The LiDAR point cloud was flown at a nominal post spacing of 1.0 meters for unobscured areas. The LiDAR data and derivative products produced are in compliance with the U.S. Geological Survey National Geospatial Program Guidelines and Base Specifications, Version 13-ILMF 2010. The flight lines were acquired by Digital Aerial Solutions, LLC. between April 02, 2012 and April 11, 2012. Derivative products from the aerial acquisition include: Raw point cloud data in LAS v1.2, classified point cloud data in LAS v1.2, bare earth surface tiles (raster DEM ESRI float GRID format), bare earth surface DEMs mosaic (raster DEM MrSID format), control points, project report, and FGDC compliant XML metadata.; abstract: The Light Detection and Ranging (LiDAR) dataset is a survey of the FY12 St Johns River Water Management LiDAR Survey, project area in north-central Florida and encompasses 800 square miles. The LiDAR point cloud was flown at a nominal post spacing of 1.0 meters for unobscured areas. The LiDAR data and derivative products produced are in compliance with the U.S. Geological Survey National Geospatial Program Guidelines and Base Specifications, Version 13-ILMF 2010. The flight lines were acquired by Digital Aerial Solutions, LLC. between April 02, 2012 and April 11, 2012. Derivative products from the aerial acquisition include: Raw point cloud data in LAS v1.2, classified point cloud data in LAS v1.2, bare earth surface tiles (raster DEM ESRI float GRID format), bare earth surface DEMs mosaic (raster DEM MrSID format), control points, project report, and FGDC compliant XML metadata.

Market Overview: The global LIDAR market is projected to reach a value of USD 1679.4114 million by 2033, exhibiting a CAGR of XX%. This growth is attributed to factors such as the increasing demand for accurate and detailed spatial data for various applications, including engineering, corridor mapping, environmental monitoring, ADAS and driverless car development, urban planning, cartography, and meteorology. Key drivers of the market include the advancements in laser scanner technology, the miniaturization of components, and the integration of LIDAR systems with other sensor modalities. Segment Analysis: The LIDAR market is segmented based on product type, system type, technology, component, functional area, company, and region. Airborne and terrestrial LiDAR are the primary product types, with airborne LiDAR holding a larger market share due to its ability to cover large areas with high accuracy. Based on system type, the market is divided into metal, polymer, and others, with metal systems being the most widely used. 1D, 2D, and 3D technologies are employed in LIDAR systems, with 3D technology offering the highest accuracy and detail. Laser scanners, navigation and positioning systems, and other components are the primary components of LIDAR systems. North America and Europe are the dominant regions in the LIDAR market, followed by Asia-Pacific and the Middle East & Africa. Recent developments include: In July 2023, Trimble launched a new cloud-based version of its log inventory and management system for forestry. A new cloud-hosted version of Trimble's popular Log Inventory and Management System (LIMS), LIMS PRO, has been released to manage the purchase of sawmill raw materials. The cloud-based log settlement system LIMS PRO is made to increase mills' operational visibility. Digitizing the workflows in the timber supply chain enables small and medium-sized forest product companies to achieve productivity and growth improvements that historically have only been accessible to major corporations., In June 2023, An innovative Bridge Collision Detection system has been unveiled by Innoviz Technologies, a provider of LiDAR sensors & perception software for automotive sector, in collaboration with Drive Group, an Israeli toll road operator. This ground-breaking approach has the potential to drastically minimize bridge and tunnel mishaps on a worldwide scale, saving lives, reducing expensive infrastructure damage, and reducing stifling traffic congestion. In comparison to current camera-based software systems that use 2D pictures to construct 3D maps of the environment and thus produce false alarms, Innoviz's Bridge Collision Detection solution provides a substantial benefit., In April 2023, FARO, a global leading name in 4D digital reality solutions, released Hybrid Reality Capture which is powered by Flash Technology and is the first solution in the AECO Markets of its kind, that delivers faster scanning for large volume projects in the field of engineering, architecture, construction and public safety applications. The most recent scan mode for Focus Premium Laser Scanner customers is Hybrid Reality Capture, which may be accessed through FARO's advanced workflows. It combines the quickness of a panoramic camera with the precision of a static 3D laser scanner., In January 2023, Teledyne Technologies, a leading provider of sophisticated digital imaging products and software, acquired ChartWorld International Limited, a global leader in providing digital marine navigation hardware and software through the most cost-competitive subscription-based model.. Notable trends are: Rising demand of 3D imagery to boost market growth.

LiDAR data for this scene was provided by RIEGAL laser measurement systems from their aerial, mobile and terrestrial LiDAR scanning platforms. A total of 3 LiDAR files were processed and published to the i3S format. Aerial data covering 3.3 Sq Km was flown for downtown Orlando. Which resulted in point cloud of nearly 70 million LiDAR points. A Mobile scan covering 0.8Km of the downtown area outputting a point cloud of 189 Million LiDAR points. A terrestrial LiDAR scan of the buildings on North Roslaind Avenue output a detailed point cloud of the buildings and street, with a point cloud of 19 million LiDAR points. These LiDAR files have been spatially matched and combined in this scene to show the differences in resolution and features captured. Bentley's Context Capture system was used to capture the Integrated mesh of the downtown Orlando area. Consisting of textured mesh of the buildings, foliage and terrain of the downtown area. The imagery for this mesh is courtesy of Track'Air.

Airborne terrestrial LiDAR was collected for St. Johns County, FL. System Parameters/Flight Plan. The LiDAR system acquisition parameters were developed based on a maximum average ground sample distance of 2.1 feet. A Leica ALS50 LiDAR sensor was used for acquisition. Acquisition specifications for the sensor follows: Field of View (full angle) - 24 degrees, Nominal flight altitude (AGL) - 30...

A first surface elevation map (also known as a Digital Elevation Model, or DEM) of a portion of St. John, U.S. Virgin Islands was produced from remotely sensed, geographically referenced elevation measurements cooperatively by the U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and National Park Service (NPS). Elevation measurements were collected over the area using the NASA Experimental Advanced Airborne Research Lidar (EAARL), a pulsed-laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters. The EAARL, developed by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 15 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When subsequent elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

For more information on Lidar science and the Experimental Advanced Airborne Research Lidar (EAARL) system and surveys, see http://ngom.usgs.gov/dsp/overview/index.php and http://ngom.usgs.gov/dsp/tech/eaarl/index.php .

Airborne Light Detection and Ranging (LiDAR) System Market Outlook

In 2023, the global airborne LiDAR system market size is estimated to be approximately USD 4.5 billion, with projections suggesting a significant growth trajectory, leading to a market value of USD 12.3 billion by 2032. This represents a compound annual growth rate (CAGR) of 11.8% from 2024 to 2032. The growth of this market is primarily driven by advancements in sensor technology, increasing demand for high-resolution topographic data, and the expanding application of LiDAR systems across various industries such as agriculture, forestry, and infrastructure development. Additionally, the integration of LiDAR with artificial intelligence and machine learning is expected to propel its adoption further.

The growth factors contributing to the expansion of the airborne LiDAR system market are multifaceted. One of the primary drivers is the increasing need for accurate and high-precision data for geographic mapping and environmental monitoring. LiDAR systems, with their ability to provide detailed three-dimensional models, are becoming essential tools for governmental and commercial applications. The rising concerns over environmental sustainability and the need for efficient resource management are pushing the demand for LiDAR technology in sectors such as forestry and agriculture. Furthermore, advancements in drone technology have made airborne LiDAR systems more accessible and cost-effective, allowing for expanded usage across various sectors.

Another critical factor fueling the market growth is the technological advancements in LiDAR sensors, which have enhanced their accuracy, range, and data acquisition capabilities. Modern LiDAR systems are now capable of operating in challenging weather conditions and diverse terrains, expanding their usability in areas such as disaster management and urban planning. Additionally, the integration of LiDAR with other technologies like GPS and inertial measurement units (IMU) has significantly improved the efficiency and effectiveness of data collection and processing. This integration is becoming increasingly important in applications requiring real-time data analysis and decision-making.

The growing adoption of LiDAR systems in the automotive and transportation industries also represents a significant growth avenue for this market. As autonomous vehicle technologies advance, the demand for reliable and accurate sensing technologies like LiDAR is expected to rise. LiDAR's ability to provide precise spatial data is crucial for safe navigation and obstacle detection in autonomous systems. Moreover, the transportation sector's shift towards smart infrastructure and intelligent transportation systems is further propelling the demand for LiDAR solutions. These systems are used for road mapping, traffic monitoring, and infrastructure assessment, enhancing safety and efficiency in transportation networks.

Airborne Laser Obstacle Avoidance Monitoring Systems are becoming increasingly crucial in the realm of aviation safety and efficiency. These systems utilize advanced laser technologies to detect and monitor potential obstacles in the flight path, providing real-time data to pilots and automated systems. This capability is particularly beneficial in challenging environments such as mountainous regions or during adverse weather conditions, where visibility may be compromised. The integration of these systems with existing avionics enhances situational awareness and decision-making, reducing the risk of collisions and improving overall flight safety. As the aviation industry continues to prioritize safety and operational efficiency, the demand for Airborne Laser Obstacle Avoidance Monitoring Systems is expected to grow, further driving innovation and development in this field.

Regionally, North America is anticipated to hold a dominant position in the airborne LiDAR system market, driven by the presence of major LiDAR manufacturers and technology innovators. The region's focus on technological advancements and infrastructure development projects provides a robust foundation for market growth. Europe is also expected to witness significant growth, supported by governmental initiatives for environmental monitoring and smart city development. The Asia-Pacific region, with its expanding infrastructure projects and increasing emphasis on resource management, is projected to exhibit the highest CAGR during the forecast period. Meanwhile, Latin America and the Middle East & Africa are grad

Lidar-derived elevation data for the Utqiagvik-Atqasuk region, Alaska, collected August 2019, Raw Data File 2022-10, provides classified point cloud, digital terrain model (DTM), surface model (DSM), and intensity model data for the communities of Utqiagvik and Atqasuk and surrounding areas. The data were collected in support of the Alaska Strategic Transportation and Resources (ASTAR) program for the purpose of investigating the potential for future road infrastructure connecting the communities. Aerial lidar data were collected between August 19 and 23, 2019, and subsequently processed using a suite of geospatial processing software. These products are released as a Raw Data File with an open end-user license. All files can be downloaded from the Alaska Division of Geological & Geophysical Surveys website (http://doi.org/10.14509/30870).

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

As part of the implementation of forest projects on the managed domain, the NFB has acquired airborne LIDAR topographic surveys over several study areas. This acquisition covers an area of approximately 8.730 ha of the Maweyo sector of the Regina St-Georges forest, in the municipality of St-Georges. The available data correspond to the raw LIDAR data (point cloud) and the derived data (MNT at 5 m, MNC at 1 m and isolined at 5 m). The derived data is directly downloadable from the GeoGuyane platform. The raw data represent a large volume and are available on request from the NFB Guyana.

Data acquired with the participation of Europe – European Agricultural Fund for Rural Development

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

Images were collected using a remotely piloted unoccupied aerial system (UAS) over the bluffs of the eastern shore of Lake Michigan in Ludington rural area, Mason County, MI. Images were collected in two separate surveys conducted on July 11, 2019, and July 14, 2021, using a DJI Phantom 3 and 4 PRO commercial UAS respectively operated by the University of Toledo. The images cover an extent between north of Chauvez Rd. to the south and north of W. Bradshaw Rd. to the north. The purpose of the survey was to monitor active bluff erosion in the area. The images are presented here in zipped files grouped by type of collection, nadir and oblique. The images were collected in JPG format and include default Exif metadata with GPS date, time, longitude and latitude, and other fields. These files were used in structure-from-motion (SfM) processing to obtain georeferenced 3D point data. The 3D derived data are in open source compressed lidar data exchange laz format and were created from the collected images using SfM photogrammetry software. A description of the laz format and links to software tools for using laz files are provided at the USGS website: https://www.usgs.gov/news/3d-elevation-program-distributing-lidar-data-laz-format.The point cloud was classified in never classified (class 0), ground (class 2), medium vegetation (class4), an water (class 9).