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 ...

All LiDAR overflights by drone were performed in June 2022. The LiDAR sensor used was a Yellowscan Surveyor and was embarked on a DJI Matrice 600 Pro UAV. The YellowScan Surveyor includes: - GNSS-inertial station : Applanix APX-15 UAV - LiDAR : Velodyne VLP16 (also known as Puck) : Wavelength: 905 nm / 300,000 pulses per second (300 kHz) / 2 echoes per pulse / Viewing angle: 360° - Accuracy: 4 cm The design of a flight path consists of a double grid with interline distances of 40 m. The drone flew at 50 m height constrained by a 5 m resolution DTM from the BD ALTI® by IGN. The flights were made at a speed of 5 m per second. All trajectories were planned with the UGCS-4.0.134 software. Then, the LiDAR dataset was post-processed in several steps by using different softwares: - Applanix POSPac UAV 8.4: post-processing (PPK) of trajectories based on the UAV's GNSS-Inertial Measurement System data and using a reference GNSS base station. The correction solution for each trajectory is exported in an ASCII SBET (Smoothed Best Estimated Trajectory) file. - Yellowscan CloudStation V2106.0.0: The SBET file is integrated in the software to generate point clouds in .las format projected in RGF93/Lambert 93. - LidR (R package): reading of las files, redrawing into tiles in compressed format .laz file corresponding to the output format. Telepilots from the TETIS resarch unit, Montpellier : Samuel Alleaume and Florian de Boissieu. This dataset includes raw data and derived raster products. The data is provided in a cloud-optimized format (COPC).

The LiDAR Point Clouds is a product that is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition projects may have been conducted by NRCan or by various partners. The LiDAR point cloud data is licensed under an open government license and has been incorporated into the National Elevation Data Strategy. Point cloud files are distributed by LiDAR acquisition project without integration between projects. The point cloud files are distributed using the compressed .LAZ / Cloud Optimized Point Cloud (COPC) format. The COPC open format is an octree reorganization of the data inside a .LAZ 1.4 file. It allows efficient use and visualization rendering via HTTP calls (e.g. via the web), while offering the capabilities specific to the compressed .LAZ format which is already well established in the industry. Point cloud files are therefore both downloadable for local use and viewable via URL links from a cloud computing environment. The reference system used for all point clouds in the product is NAD83(CSRS), epoch 2010. The projection used is the UTM projection with the corresponding zone. Elevations are orthometric and expressed in reference to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).

Airborne remote sensing data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) hexacopter platform operated by NGEE Arctic scientists from the EES-14 group at Los Alamos National Laboratory. These data were collected in July 2018 at a field site near mile marker 64 along the Kougarok road (Nome-Taylor Highway) between Nome, Alaska and Taylor, Alaska. A DJI Matrice 600 Pro Airframe and Routescene UAV LiDARSystem was used to collect LiDAR data. The LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). This data package contains point clouds (.laz), processing metadata files (json.lvp), and post-processed kinematic files (.csv). Ancillary aircraft data, flight mission parameters, weather conditions, raw LiDAR data, and RGB imagery can be found in NGA298. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Note: To better find the files to download, select "Change View: Tree". This dataset is associated with the paper "TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds" published in Ecological Informatics and the ML4RS workshop paper "Towards general deep-learning-based tree instance segmentation models" presented at ICLR 2024. It extends the publicly available segmented tree data that was introduced by Calders et al. [1] and Tockner et al. [2]. These two publications only provide segmented trees. For this dataset, these tree labels were propagated to the original point clouds and the remaining points were automatically classified as either "non-tree points" or "unlabeled". Furthermore, some manual correction of the segmented trees was conducted, especially for the tree bases in Tockner et al. [2]. A more comprehensive description of the dataset is given in the linked publications. We provide the laser scans in the original resolution as well as in a voxelized form where the point cloud has been subsampled to contain only one point within a cube with edge length 0.1m. We provide the forest laser scans in the .laz format and follow the same labeling scheme proposed by Puliti et al. [3]. Specifically, a unique identifier is stored as an additional field named "treeID" in the .laz files. Trees are labeled starting from 1 and all non-tree points have the label 0 in the treeID field. The dataset comes with a classification into the three semantic categories "non-tree-points" (label=2), "unlabeled" (label=3) and "tree-points" (label=4) that is saved in the classification field of the .laz file. The .laz format is compatible with popular point cloud processing tools like CloudCompare and can also be loaded in python using the laspy package. Example code for opening .laz files in python as numpy arrays is provided in the open_files.ipynb notebook. References [1] Calders, K., Origo, N., Burt, A., Disney, M., Nightingale, J., Raumonen, P., ... & Lewis, P. (2018). Realistic forest stand reconstruction from terrestrial LiDAR for radiative transfer modelling. Remote Sensing, 10(6), 933. [2] Tockner, A., Gollob, C., Kraßnitzer, R., Ritter, T., & Nothdurft, A. (2022). Automatic tree crown segmentation using dense forest point clouds from Personal Laser Scanning (PLS). International Journal of Applied Earth Observation and Geoinformation, 114, 103025. [3] Puliti, S., Pearse, G., Surový, P., Wallace, L., Hollaus, M., Wielgosz, M., & Astrup, R. (2023). FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees. arXiv preprint arXiv:2309.01279.

The LiDAR Point Clouds is a product that is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition projects may have been conducted by NRCan or by various partners. The LiDAR point cloud data is licensed under an open government license and has been incorporated into the National Elevation Data Strategy. Point cloud files are distributed by LiDAR acquisition project without integration between projects. The point cloud files are distributed using the compressed .LAZ / Cloud Optimized Point Cloud (COPC) format. The COPC open format is an octree reorganization of the data inside a .LAZ 1.4 file. It allows efficient use and visualization rendering via HTTP calls (e.g. via the web), while offering the capabilities specific to the compressed .LAZ format which is already well established in the industry. Point cloud files are therefore both downloadable for local use and viewable via URL links from a cloud computing environment. The reference system used for all point clouds in the product is NAD83(CSRS), epoch 2010. The projection used is the UTM projection with the corresponding zone. Elevations are orthometric and expressed in reference to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).

These 5000 foot tile index polygons define the boundaries of individual point cloud (.laz) files, originally produced for the New Jersey Orthophoto Mapping Program 2007-2008. The tile scheme is in New Jersey State Plane coordinates, NAD83, in units of US Survey feet. This index is the same as the index for the 2002 - 2003 New Jersey Orthophoto Mapping Program. Attributes include tile names, file size in KB & MB, and links to download.

Minimally processed Light Detection and Ranging (LiDAR) point cloud data were collected from Los Alamos National Laboratory’s (LANL) heavy-lift unoccupied aerial system (UAS) quadcopter and hexacopter platforms operated by Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) scientists from the EES-14 group at LANL. These data were collected in August 2017 and July 2018 at the NGEE Arctic field site near mile marker 27 of the Bob Blodgett Nome–Teller Memorial Highway between Nome, Alaska and Teller, Alaska. A Vulcan Raven X8 Airframe (Mitcheldean, Gloucestershire, UK), DJI Matrice 600 Pro Airframe (Shenzhen, China), and Routescene UAV LiDARSystem (Edinburgh, Scotland, UK) were used to collect LiDAR data. This LiDAR data has undergone basic post-processing using Routescene LidarViewer Pro software to create point cloud data (.laz files). Following basic processing, data from multiple flights and years were merged into a single raw point cloud using CloudCompare software. This data package contains compressed point clouds (.laz), post-processed kinematic files (.csv), and details about the Level 1 (L1) processing workflow. Ancillary aircraft data, flight mission parameters, weather conditions, and raw LiDAR data and imagery can be found in the L0 datasets for these campaigns: NGA299 (2017) and NGA297 (2018).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a researchmore » effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).« less

Point cloud data collected along a 500 meter portion of the 2014 South Napa Earthquake surface rupture near Cuttings Wharf Road, Napa, CA, USA. The data include 7 point cloud files (.laz). The files are named with the location and date of collection and either ALSM for airborne laser scanner data or TLS for terrestrial laser scanner data. The ALSM data re previously released but are included here because they have been precisely aligned with the TLS data as described in the processing section of this metadata. The included files are: Napa_CuttingsWharf_TLS_31082015_utm.laz Napa_CuttingsWharf_TLS_27022015_utm.laz Napa_CuttingsWharf_TLS_26082014_utm.laz Napa_CuttingsWharf_TLS_22102014_utm.laz Napa_CuttingsWharf_TLS_15092014_utm.laz Napa_CuttingsWharf_ALSM_09092014_utm.laz Napa_CuttingsWharf_ALSM_xx052003_utm.laz

The DFG Priority Program 1803 “EarthShape” (www.earthshape.net) investigates Earth surface shaping by biota. As part of this project, we present Light Detection and Ranging (LiDAR) data of land surface areas for the four core research sites of the project. The research sites are located along a latitudinal gradient between ~26 °S and ~38 °S in the Chilean Coastal Cordillera. From north to south, the names of these sites are: National Park Pan de Azúcar; Private Reserve Santa Gracia; National Park La Campana; and National Park Nahuelbuta. The three datasets contain raw 3D point cloud data captured from an airborne LiDAR system, and the following derivative products: a) digital terrain models (DTM, sometimes also referred to as DEM [digital elevation model]) which are (2.5D) raster datasets created by rendering only the LiDAR returns which are assumed to be ground/bare-earth returns and b) digital surface models (DSM) which are also 2.5D raster datasets produced by rendering all the returns from the top of the Earth’s surface, including all objects and structures (e.g. buildings and vegetation). The LiDAR data were acquired in 2008 (southernmost Nahuelbuta [NAB] catchment), 2016 (central La Campana [LC] catchment) and 2020 (central Santa Gracia [SGA] catchment). Except for Nahuelbuta (data already was available from the data provider from a previous project), the flights were carried out as part of the "EarthShape" project. The LiDAR raw data (point cloud/ *.las files) were compressed, merged (as *.laz files) and projected using UTM 19 S (UTM 18 S for the southernmost Nahuelbuta catchment, respectively) and WGS84 as coordinate reference system. A complementary fourth dataset for the northernmost site in the National Park Pan de Azúcar, derived from Uncrewed Aerial Vehicle (UAV) flights and Structure from Motion (SfM) photogrammetry, is expected to be obtained during the first half of 2022 and will be added to the above data set.

This deposit includes Bare-earth airborne lidar data collected in 2015, from the south flank of the Santa Ynez Range, north of Montecito, CA, USA. The raw data, collected in 2015 using a Geiger-mode LiDAR scanner, was purchased from Harris Corporation Archive with the Montecito Partners for Resilient Communities fund. Data were subsequently processed using LasTool software. This software was used to clip to the six mountainous watersheds that experienced debris flows during January 2018 and to isolate only classified ground points. The data files series of .laz files encompass an area on the south flank of Santa Ynez Mountains north of Montecito, CA, USA, near 34.4206, -119.6363 in degrees longitude and latitude.

This dataset contains 11 terrestrial laser scanning (TLS) tree point clouds (in .LAZ format v1.4) of 7 different species, which have been manually labeled into leaf and wood points. The labels are contained in the Classification field (0 = wood, 1 = leaf). The point clouds have additional attributes (Deviation, Reflectance, Amplitude, GpsTime, PointSourceId, NumberOfReturns, ReturnNumber). Before labeling, all point clouds were filtered by Deviation, discarding all points with a Deviation greater than 50. An ASCII file with tree species and tree positions (in ETRS89 / UTM zone 32N; EPSG:25832) is provided, which can be used to normalize and center the point clouds. This dataset is intended to be used for training and validation of algorithms for semantic segmentation (leaf-wood separation) of TLS tree point clouds, as done by Esmorís et al. 2023 (Related Publication). The point clouds are a subset of a larger dataset, which is available on PANGAEA (Weiser et al. 2022b, see Related Dataset). More details on data acquisition and processing, file formats, and quality assessments can be found in the corresponding data description paper (Weiser et al. 2022a, see Related Material).

An aerial photogrammetry survey and laser scan of approx. 1 square kilometre of Newry City core, with ground sample distance of 40mm. Dataset presented as a fully orthorectified image in .kmz and .tiff file formats. The aerial scan dataset is point cloud data in a series raw .laz files, with points stored as x, y, z where x and y are longitude and latitude, and z is the elevation. The dataset’s were captured by a remote drone aerial survey carried out in August 2018 by Aquila UAS on behalf of Newry Mourne & Down District Council to aid in the development of their proposals to regenerate Newry City core. Newry Mourne & Down District Council currently has no plans to update this dataset.

LIDAR (.LAZ x2 files): data from Cape Bruguieres Channel, as published in Benjamin et al (2020) PLOS ONE article. From the Article: “the team deployed a Diamond Aircraft HK36TTC-ECO Dimona motorglider with two LiDAR systems mounted in under-wing pods: a Riegl Q680i-S (topographic) and a Riegl VQ-820-G (topo-bathymetric), each combined with a tactical grade IMU/GPS system (Novatel SPAN ISA/LCI). A Canon 5D Mk4 was fitted with an EF 24 mm (f/1.4LII USM) lens and co-mounted with the Q680i-S. Point cloud density ranged between 10 and 20 points/m2, and data was processed and converted to a Digital Elevation Model (DEM) using the Global Mapper LiDAR module."

GCP (.xls 1 file): Trimble Net R9 dGPS data with Trimble RTX Satellite Subscription. Data acquired by the Deep History of Sea Country Project Team.

These 5000 foot tile index polygons define the boundaries of individual point cloud (.laz) files, originally produced for the New Jersey Orthophoto Mapping Program 2007-2008. The tile scheme is in New Jersey State Plane coordinates, NAD83, in units of US Survey feet. This index is the same as the index for the 2002 - 2003 New Jersey Orthophoto Mapping Program. Attributes include tile names, file size in KB & MB, and links to download.

Single photon lidar light detection and ranging (SPL LiDAR) is an active remote sensing technology for: * mapping vegetation aspects including cover, density and height * representing the earth's terrain and elevation contours We acquired SPL data on an airborne acquisition platform under leaf-on conditions to support Forest Resources Inventory (FRI) development. FRI provides: * information to support resource management planning and land use decisions within Ontario’s Managed Zone * information on tree species, density, heights, ages and distribution The SPL data point density ranges from a min of 25pts/m. Each point represents heights of objects such as: * ground level terrain points * heights of vegetation * buildings The lidar was classified according to the Ontario lidar classifications. Low, medium and tall vegetation are classed as 3, 4, 5 and 12 classes. The FRI SPL products include the following digital elevation models: * digital terrain model * canopy height model * digital surface model * intensity model (signal width to return ratio) * forest inventory raster metrics * forest inventory attributes * predicted streams * hydro break lines * block control points Lidar fMVA data supports developing detailed 3D analysis of: * forest inventory * terrain * hydrology * infrastructure * transportation * other mapping applications We made significant investments in Single Photon LiDAR data, now available on the Open Data Catalogue. Derivatives are available for streaming or through download. The map reflects areas with LiDAR data available for download. Zoom in to see data tiles and download options. Select individual tiles to download the data. You can download: * classified point cloud data can also be downloaded via .laz format * derivatives in a compressed .tiff format * Forest Resource Inventory leaf-on LiDAR Tile Index. Download | Shapefile | File Geodatabase | GeoPackage Web raster services You can access the data through our web raster services. For more information and tutorials, read the Ontario Web Raster Services User Guide. If you have questions about how to use the Web raster services, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Note: Internal users replace "https://ws.” with “https://intra.ws." * CHM https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_CHM_SPL/ImageServer * DSM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DSM_SPL/ImageServer * DTM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DTM_SPL/ImageServer * T1 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T1/ImageServer * T2 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T2/ImageServer * Landcover - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServer

Point clouds collected at the of the Forest and Biodiversity Experiment 2 located at the Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota, USA. These point clouds were collected using a LiDAR onboard an Uncrewed Aerial Vehicle (UAV) thoughout the growing season of 2022. , We collected LiDAR data across the FAB2 experiment eight times during 2022 on the following days of the year: 100, 138, 163, 188, 215, 250, 261, and 297. Flights were conducted throughout the growing season before leaf out until after leaf senescence of the deciduous species in accordance with sufficient accumulated growing degree days for 2022. Data were collected using a Zenmuse L1 sensor onboard an Uncrewed Aerial Vehicle (UAV) DJI Matrice 300. This sensor integrates a Livox LiDAR module, an inertial measurement unit, and an RGB camera on a 3-axis stabilized gimbal. The LiDAR module has a conic footprint on ground with a field of view of 77.2° vertical and 70.4° horizontal, enabling it to capture multiple returns. All the surveys were conducted using autonomous flights programmed to take place at a speed of 8 m/s at 50 m above ground. These surveys were done in an area of ~5.8 ha with 85% overlap between sidetracks allowing capture of dense point clouds (~ 2100 point/m2). During thes..., , # UAV-LiDAR point clouds from the Forest and Biodiversity Experiment 2 (2022)

https://doi.org/10.5061/dryad.jdfn2z3hk

Eight point clouds were collected on the Forest and Biodiversity Experiment 2 throughout the 2022 growing season

Description of the data and file structure

We provide point clouds using the compressed format (.LAZ) clipped to the area where the experiment is located. These point clouds were not filtered by noise nor decimated by the redundancy of points. However, these were processed by stripe alignment, ground classification, and normalization by height. The height provided (i.e., Z coordinate) is in reference to the height from the ground. The X and Y columns are coordinates projected to NAD83 UMT15 (EPSG:26915). The names of the files refers to the date of data collection.

Code/Software

Point clouds can be opened using QGIS, [CloudCompare,](https://www.danielgm.net...

The Virginia LiDAR Inventory Web Mapping Application provides access to LiDAR point cloud and individual project metadata collected in the Commonwealth of Virginia according to the USGS 3DEP specification. Data is obtained from NOAA, USGS, and VGIN data portals. LiDAR Point Clouds are compressed for file storage and transfer. USGS and NOAA utilize the compressed .LAZ format. This dataset will provide the end user a necessary set of geographic extents that can be used with an ArcGIS Desktop or Pro session to select by location specific areas of download. The downloads can either be batch processed by the analysis with scripting and modeling or individual tiles can be downloaded. This is the tile data powering VGIN ArcGIS server services utilized in the VGIN LiDAR Download Application.

These 500 m x 500 m tile index polygons define the boundaries of individual point cloud (.laz) files. The tile scheme is in NAD 1983 (NSRS 2007) geographic coordinate system. Attributes include tile names and links to download.

This part of the data release presents georeferenced elevation point clouds spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the point clouds were derived, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected with precise Global Positioning System (GPS) navigation data from a manned aircraft and were then processed using structure-from-motion (SFM) methods as described in Nolan and others, 2015. The included files contain georeferenced point cloud data in .laz format. The point clouds were converted to .laz format by the USGS. Users are encouraged to use the Tile Index shapefile, which is also available in this data release, to identify elevation point cloud files that are appropriate to a specific area of interest.