Height of the top of canopy above bare earth (Canopy Height Model; CHM). The CHM is derived from theNEON LiDAR point cloud and is generated by creating a continuous surface of canopy height estimates across the entire spatial domain of the LiDAR survey. The point cloud is separated into classes …

The global navigation map market is experiencing robust growth, driven by increasing adoption of location-based services across various sectors. Our analysis projects a market size of $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The automotive industry's reliance on advanced driver-assistance systems (ADAS) and autonomous vehicles is a primary driver, demanding high-precision and regularly updated map data. Furthermore, the proliferation of mobile devices with integrated GPS and mapping applications continues to stimulate market growth. The burgeoning enterprise solutions segment, utilizing navigation maps for logistics, fleet management, and delivery optimization, contributes significantly to overall market value. Government and public sector initiatives promoting smart cities and infrastructure development further fuel demand. Technological advancements, such as the integration of LiDAR and improved GIS data, enhance map accuracy and functionality, attracting more users and driving market expansion. The market segmentation reveals substantial contributions from various application areas. The automotive segment is projected to maintain its dominance throughout the forecast period, followed closely by the mobile devices and enterprise solutions segments. Within the type segment, GIS data holds a significant market share due to its versatility and application across various sectors. However, LiDAR data is experiencing rapid growth, driven by its high precision and suitability for autonomous driving applications. Geographic regional analysis indicates strong market presence in North America and Europe, primarily driven by advanced technological infrastructure and high adoption rates. However, the Asia-Pacific region is poised for substantial growth, fueled by rapid urbanization, increasing smartphone penetration, and government investments in infrastructure development. Competitive landscape analysis reveals a blend of established players and emerging technology companies, signifying an increasingly dynamic and innovative market environment.

The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from LIDAR data taken in the spring between 2007 and 2012. It contains ground level samples with all other items above ground (such as buildings, bridges, trees etc.) removed. This version is interpolated; the areas where objects …

Digital models of the surface (DSM) and terrain (DTM) derived from NEON LiDAR data. DSM: Surface features (topographic information with vegetation and man-made structures present). DTM: Bare earth elevation (topographic information with vegetation and man-made structures removed). Images are given in meters above mean sea level and mosaicked onto a spatially uniform grid at 1 m resolution. See NEON Data Product DP3.30024.001 for more details. Documentation: Elevation - LiDAR (DP3.30024.001) Quick Start Guide Get started by exploring the Intro to AOP Data in Google Earth Engine Tutorial Series Browse and interact with AOP data in the NEON AOP GEE Data Viewer App

Two 30-m resolution aboveground woody biomass (AGWB) model for the Brazilian Cerrado biome built based on optical satellite imagery (Landsat-5 and Landsat-8) and SAR imagery (ALOS and ALOS-2), using two machine learning algorithms (Random Forest and CART). Field data used to calibrate the models include and a set of plot-based and LiDAR-derived AGWB estimates (n=1,858) from a wide network of researchers in Brazil

This data set is derived from the original 2005 B4 lidar dataset collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a \"bare-earth\" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief), \"full-feature\" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format. The reprocessing of the 2005 B4 dataset was performed by Dr. Stephen B DeLong, USGS Earthquake Hazards Program, as a service to the community. The data available here were originally published on the USGS ScienceBase website as Classified point cloud and gridded elevation data from the 2005 B4 Lidar Project, southern California, USA.

Original B4 project description: The B4 Lidar Project collected lidar point cloud data of the southern San Andreas and San Jacinto Faults in southern California. Data acquisition and processing were performed by the National Center for Airborne Laser Mapping (NCALM) in partnership with the USGS and Ohio State University through funding from the EAR Geophysics program at the National Science Foundation (NSF). Optech International contributed the ALTM3100 laser scanner system. UNAVCO and SCIGN assisted in GPS ground control and continuous high rate GPS data acquisition. A group of volunteers from USGS, UCSD, UCLA, Caltech and private industry, as well as gracious landowners along the fault zones, also made the project possible. If you utilize the B4 data for talks, posters or publications, we ask that you acknowledge the B4 project. The B4 logo can be downloaded here. More information about the B4 Project.

Publications associated with this dataset can be found at NCALM's Data Tracking Center

Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest surrounding Crested Butte, CO with remotely sensed data, including LiDAR. The flight box design encompassed the watersheds, surveying a total area of 334 km2 across 72 lines. The instrument used was an Optech Gemini, with a pulse density of 2-9 pulses m-2 across the study area (see final report document for detailed information). These LiDAR data are the primary data that were provided by NEON including LAZ files of a classified point cloud, and geotifs of a digital surface elevation model, digital terrain elevation model, and a canopy height model at 1 m resolution with a height threshold of > 2 m. Raster files can also be found on Google Earth Engine: https://code.earthengine.google.com/5c96bbc96ffd50e3c8b1433b34a0bb86.

description: This data set is derived from the original 2005 data collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief) , "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format.; abstract: This data set is derived from the original 2005 data collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief) , "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format.

The Digital Elevation Model (DEM) 5 meter Grid of Australia derived from LiDAR model represents a National 5 meter (bare earth) DEM which has been derived from some 236 individual LiDAR surveys between 2001 and 2015 covering an area in excess of 245,000 square kilometers. These surveys cover Australia's populated …

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission (https://gliht.gsfc.nasa.gov/) utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Digital Terrain Model Keyhole Markup Language (KML) data product (GLDTMK) is to provide LiDAR-derived bare earth elevation, aspect and slope on the EGM96 Geopotential Model. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and the collection will continue to grow as aerial campaigns are flown and processed. GLDTMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the digital terrain with a color map applied in JPEG format.

Terrestrial LiDAR data collected by the team at University College London.

This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices

UCL project name: 2017-02-08.001.riproject

Plot ID: RSQ

State or region: London

Date project started: 2/8/2017

Instrument: UCL RIEGL VZ-400

Scan pattern: Spiral (11 pos)

Angular resolution: 0.04

Images captured: Yes

Number of scans: 22.0

Google Maps URL: https://www.google.co.uk/maps/place/Russell+Square+Gardens/@51.5216334,-0.1261532,20.51z/data=!4m13!1m7!3m6!1s0x48761b2e1672c317:0x2eb39a3b3d33d9f5!2sMalet+St,+London!3b1!8m2!3d51.5214099!4d-0.1302396!3m4!1s0x48761b313fbfd0a1:0x494a624c4d12c02f!8m2!3d51.5216396!4d-0.1259804

Publications: https://doi.org/10.1186/s13021-018-0098-0

For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017

Please acknowldege the producers of this data set if using this data for publication.

This Light Detection and Ranging (LiDAR) LAS dataset is a topographic survey conducted for the St. Louis District of the Army Corps of Engineers and the US Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) of Pine County, Minnesota. The data were collected October 25 - November 4, 2006. The data cover approximately 1435 square miles and were collected at an average of 3 meters point spacing. The LAS points in this data set are classified as unclassified and ground. A footprint of the coverage for this data set may be viewed in Google Earth at: ftp://coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid12a/data/533/supplemental/2006_Pine_County_Minnesota_Lidar.kmz

FRUC Datasets (Forest environment dataset)

This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (https://www.youtube.com/@forestryroboticsuc; part of the Institute of Systems and Robotics, https://www.isr.uc.pt/) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; http://semfire.ingeniarius.pt/) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms;

The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the Choupal National Woods, Coimbra, Portugal (40^◦13′13.3′′N;8^◦26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing two circular loop laps amounting to a total distance of approximately 800m, with a total duration of 24 minutes and 26 seconds. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into rosbags, a file format used by the ROS (Robot Operating System) to record and play back data.

More specifically, the datasets include:

• RGB Images from an Intel Realsense D435i
• Aligned Depth Images from an Intel Realsense D435i
• Point Clouds from a Livox Mid-70 LiDAR
• Unfiltered acceleration, gyroscopic and magnetic data from a Xsens MTi IMU
• Unfiltered acceleration, gyroscopic data from an Intel Realsense D435i

Description of files:

1. The dataset was split into three rosbags (choupal_0.bag, choupal_1.bag, choupal_2.bag).
2. The rosbag_info_[x].txt contains the information of each rosbag;
3. The sensor_box.urdf contains all the required transforms;
4. The sensor_box.stl contains the 3D model of the apparatus;
5. The choupal.launch publishes the sensor transforms and plays the dataset;
6. The localization.bag contains the final graph of poses extracted with RTAB-Map republished as nav_msgs/odom. These poses are more accurate than the localization_with discrete_jumps.bag but are recorded at a lower frequency;
7. The localization_with discrete_jumps.bag contains a map-referenced localization extracted with RTAB-Map, containing more entries than localization.bag but it contains discrete jumps (not ideal for pure mapping libraries).

Usage:

1. Extract the fruc_dataset_choupal_launch.zip into a catkin workspace
2. Copy the rosbags into the fruc_dataset_choupal_launch/rosbag/
3. Edit the fruc_dataset_choupal_launch/launch/choupal.launch file to your use case:
   1. Change the file_path argument if the rosbags are not in the default location;
   2. Set enable_localization to true in order to use the provided localization bags.
4. Compile the package and source the environment:

   1. catkin_make [/your_catkin_workspace/]

   2. source [/your_catkin_workspace/devel/setup.bash]

5. Launch the files:

   roslaunch fruc_dataset_choupal_launch choupal.launch

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands).The algorithm setting group selection used for GEDI02_A Version 2 has been modified for evergreen broadleaf trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. Please see User Guide for more information. The Global Ecosystem Dynamics Investigation GEDI mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. The GEDI instrument, attached to the International Space Station (ISS), collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of the 3-dimensional structure of the Earth. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. ProductDescriptionL2A VectorLARSE/GEDI/GEDI02_A_002L2A Monthly rasterLARSE/GEDI/GEDI02_A_002_MONTHLYL2A table indexLARSE/GEDI/GEDI02_A_002_INDEXL2B VectorLARSE/GEDI/GEDI02_B_002L2B Monthly rasterLARSE/GEDI/GEDI02_B_002_MONTHLYL2B table indexLARSE/GEDI/GEDI02_B_002_INDEXL4A Biomass VectorLARSE/GEDI/GEDI04_A_002L4A Monthly rasterLARSE/GEDI/GEDI04_A_002_MONTHLYL4A table indexLARSE/GEDI/GEDI04_A_002_INDEXL4B BiomassLARSE/GEDI/GEDI04_B_002

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.

The Actueel Hoogtebestand Nederland (AHN) is a dataset with detailed and precise elevation data for the whole of the Netherlands. Elevation information was collected from helicopters and aircraft using laser technology with vertical accuracy of 5 cm. AHN3 Dataset contains the Netherlands AHN 0.5m DSM and DTM variables. The data cover the period between 2014 and 2019. The Digital Terrain Model (DTM) product represents the elevation of the ground, while the Digital Surface Model (DSM) product represents the elevation of the tallest surfaces at that point.

The Global Multi-Resolution Topography (GMRT) synthesis is a multi-resolutional compilation of edited multibeam sonar data collected by scientists and institutions worldwide, that is reviewed, processed and gridded by the MGDS Team and merged into a single continuously updated compilation of global elevation data. The synthesis began in 1992 as the Ridge Multibeam Synthesis (RMBS), was expanded to include multibeam bathymetry data from the Southern Ocean, and now includes bathymetry from throughout the global and coastal oceans. GMRT is included in the ocean basemap in Google Earth (since June 2011) and the GEBCO 2014 compilation.

Data is accessed through the GMRT GridServer Web Service. OpenTopography provides a user interface for using the web service and enables users to utilize OpenTopography processing tools, such as visualization and advanced hydrologic terrain analysis (TauDEM).

このデータセットは、直径 25 m の lidar フットプリントに対応した NASA グローバル エコシステム ダイナミクス調査（GEDI）レベル 2 および 4A プロダクトから得られた、分析にすぐに使用できる、ほぼ全世界を対象としたマルチ解像度グリッド化植生構造指標で構成されています。このデータセットは、単一のソースに基づいて、ほぼ地球全体の植生構造を包括的に表しています。

Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

The Automotive 3D Map System market is experiencing robust growth, driven by the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies. The market size in 2025 is estimated at $10 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors: the escalating demand for enhanced navigation and location-based services, the proliferation of connected cars, and the imperative for improved safety and efficiency in autonomous vehicles. The integration of high-definition 3D maps into vehicle systems allows for precise localization, improved route planning, and the enablement of crucial safety features like lane keeping assist and automatic emergency braking. Furthermore, the continuous advancements in sensor technologies, such as LiDAR and radar, are contributing to the increased accuracy and detail of 3D maps, further accelerating market growth. Significant investments from both established automotive players and technology companies are fueling innovation and competition within the sector. Market segmentation reveals a dynamic landscape, with in-dash navigation systems holding a significant share currently, followed by portable navigation devices. However, the application in passenger vehicles dominates the market, albeit with substantial growth anticipated in light commercial vehicles, heavy-duty trucks, buses, and off-road vehicles. Geographic distribution shows strong market penetration in North America and Europe, driven by early adoption of autonomous vehicle technologies and stringent safety regulations. However, the Asia-Pacific region is projected to experience the fastest growth, fueled by increasing vehicle production and rising disposable incomes in developing economies. Restraints to growth include high initial investment costs for 3D mapping infrastructure and the need for robust cybersecurity measures to safeguard sensitive location data. Despite these challenges, the long-term outlook remains positive, with continued technological advancements and increasing government support paving the way for sustained expansion of the automotive 3D map system market.