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390 results for “LiDAR data”
Micropulse Lidar Data
<p>The lidar data were measured over Beijing, a megacity located in the North China Plain. Due to incomplete laser pulse corrections, the near-surface lidar blind zone is ∼0.15 km. Background subtraction, saturation, after-pulse, overlap, and range corrections are applied to raw MPL data to calculate the normalized signals. For the correction and quality control, the readers may contact Tianning Su (tianning@umd.edu). </p>
LiDAR and thermal data for camera pose estimation using the depth-map correspondence algorithm
<p>Folder and file structure:</p> <ul> <li>lidar_roi.ply : ~360 MB mesh file which is a sub-part of the whole Orlova Chuka scan collected in [1]</li> <li>yyyy-mm-dd total of ~17 GB. All video data including raw data, exported video, digitised xy points and calibration results <ul> <li>2018-08-19</li> <li>2018-08-17</li> <li>2018-08-14</li> <li>2018-07-28</li> <li>2018-07-25</li> <li>2018-07-21</li> </ul> </li> </ul> <p><em>Thermal camera YYYY-MM-DD folder substructure</em>: Each of the yyyy-mm-dd dates is one recording session. Each session folder has the following structure:</p> <ul> <li>avi_files (present on some nights)</li> <li>cave_photos: (present on some nights)</li> <li>mic_and_wall_points</li> <li>tmc_files: (present on some nights) The TMC files is a proprietary format to store thermal camera video data (TeAx GmbH, Germany). on 2018-08-17, only P0000000 is provided as it doesnt' have humans blocking the scene. Each frame can be exported to csv using the ThermoViewer tool, downloadable at: https://thermalcapture.com/thermoviewer-download/</li> <li>video_calibration: results and associated data to get DLT coefficients estimated using the easyWand [2] workflow. <ul> <li>image : csv file with pixel values of the images used for annotations</li> <li>mics : 2D point locations of mics placed on the cave walls</li> <li>other_cave_surface : other points on the cave surface that were pointed at <ul> <li>calibration_output: results from easyWand runs. Choose the highest round number <ul> <li>yyyy-mm-dd_roundX_<wandscore>_cam1Tforms.mat (undistortion files)</li> <li>yyyy-mm-dd_roundX_<wandscore>_cam2Tforms.mat</li> <li>yyyy-mm-dd_roundX_<wandscore>_cam3Tforms.mat</li> <li>yyyy-mm-dd_roundX_<wandscore>_dltCoefs.csv (each column is one camera's DLT coefficients)</li> <li>yyyy-mm-dd_roundX_<wandscore>_easyWandData.mat (easyWand session file)</li> </ul> </li> <li>gravity: (mostly there) video and xy points for a falling object to align the calbiration to gravity. Output from DLTdv7 clicking session.</li> <li>wand: video and xy points of the 'wand' calibration object. Output from DLTdv7 clicking session.</li> <li>camera_intrinsic.txt or thermalcam_camprofiles_profile.txt : the camera instrinsics</li> </ul> </li> </ul> </li> </ul> <ul> <li>alignment_results: ~887 MB zipped folder. <ul> <li>dmcp_experiments: the results of DMCP alignment <ul> <li>round_01 : <em>ignore this folder</em></li> <li>round_03 : <em>ignore this folder</em></li> <li>round_05: here yyyy-mm-dd is short for all other nights. Each yyyy-mm-dd folder has multiple csv files. The 'transform.csv' is the most relevant file, as it holds the transformation matrix to move 3D points from camera triangulations into the LiDAR coordinate system. <ul> <li>2018-07-21--cam0</li> <li>2018-07-21--cam1</li> <li>2018-07-21--cam2</li> <li>yyyy-mm-dd--cam0</li> <li>yyyy-mm-dd--cam1</li> <li>yyyy-mm-dd--cam2</li> <li>...</li> <li>...</li> <li>...</li> <li>2018-08-19--cam0</li> <li>2018-08-19--cam1</li> <li>2018-08-19--cam2</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p>CITATION: If you use this dataset for your research please cite this Zenodo dataset and the accompanying paper.</p> <p>This uploaded dataset is part of the <em>Ushichka</em> dataset [3]. The audio-video system was designed by Holger R. Goerlitz. The LiDAR data was collected by Asparuh Kamburov. Video data collected by Thejasvi Beleyur.</p> <p>References</p> <p>[1] : Kamburov, A., Goerlitz, H. R., Beleyur, T 2018, Geospatial modelling inside the "Orlova Chuka" cave in Bulgaria, <em>non-peer reviewed conference contribution</em>, XXVIII International Symposium on Modern Technologies and Professional Practise in Geodesy and related fields</p> <p>[2]: Theriault, D. H., Fuller, N. W., Jackson, B. E., Bluhm, E., Evangelista, D., Wu, Z., M., Betke & Hedrick, T. L. (2014). A protocol and calibration method for accurate multi-camera field videography. <em>Journal of Experimental Biology</em>, <em>217</em>(11), 1843-1848.</p> <p>[3]: Beleyur Thejasvi, 2021. Theoretical and empirical investigations of echolocation in bat groups, PhD dissertation, University of Konstanz (<a href="http://nbn-resolving.de/urn:nbn:de:bsz:352-2-q41u3qlu1em03">http://nbn-resolving.de/urn:nbn:de:bsz:352-2-q41u3qlu1em03</a>)</p>
Graph-based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds: Labeled validation data
<p>Set of 10 manually labeled point clouds used in the performance assessment of "Graph-based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds". This dataset is a collection of single trees scanned from different regions covering tropical, temperate, and boreal species—with heights ranging from 5.4 to 43.7 m, using the Riegl VZ-400 and Leica ScanStation C10 terrestrial laser scanner. These data have been preprocessed. For the original data, see https://doi.org/10.5061/dryad.10hq7;<br> https://doi.org/10.5061/dryad.np5hqbzp6;<br> https://doi.org/10.1594/PANGAEA.942856.</p>
Lidar data at Sapelo Island, Georgia, USA
<p>This is the data corresponding to figure 9 of the paper:</p> <p>Sergio Fagherazzi, Giulio Mariotti, Nicoletta Leonardi, Alberto Canestrelli, William Nardin, William S. Kearney, Morphodynamics of salt marshes in a period of accelerated sea level rise, Submitted to Journal of Geophysical Research, Centennial Edition.</p>
Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar (data and code)
<p>The archive contains supplemental material for the article "Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar" by Kelberlau and Mann:</p> <p>- Windcube RAW and 10-min averaged data</p> <p>- Ultrasonic anemometer data from the meteorological mast in Høvsøre</p> <p>- Monin-Obukhov length data</p> <p>- windsimu input files, a windsimu executable for unix systems to create turbulence boxes and a windsimu manual</p> <p>- Matlab scripts for data processing and visualization</p>
UAV LiDAR and trajectory data from Antiochia ad Cragum
<p>This data set includes LiDAR and trajectory data from four flights of an area located roughly north-east of the Small Bath Area at Antiochia ad Cragum in south-central Turkey in 2022 using a custom LIDAR sensor built with a Livox Mid-40 LiDAR camera and open source hardware and software from the OpenMMS project. Each zip file includes two adjacent flights oriented either principally east-west or north-south. The four flights together offer overlapping coverage of the area. In addition to the LiDAR files trajectory information collected with an AceInna OpenIMU and two Ublox ZED-F9P GNSS modules is included.</p>
Terretrial LiDAR data collected from Highgate Cemetery, London, UK
<p>Terrestrial LiDAR data collected by the team at University College London.</p> <p>This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices</p> <p><strong>UCL project name</strong>: 2017-08-10.001.riproject</p> <p><strong>Plot ID</strong>: HGC</p> <p><strong>State or region</strong>: London</p> <p><strong>Date project started</strong>: 8/10/2017</p> <p><strong>Area scanned</strong>: 75 m x 75 m</p> <p><strong>Instrument</strong>: UCL RIEGL VZ-400</p> <p><strong>Scan pattern</strong>: 25 positions</p> <p><strong>Angular resolution</strong>: 0.04</p> <p><strong>Images captured</strong>: Yes</p> <p><strong>Number of scans</strong>: 50.0</p> <p><strong>Publications</strong>: https://doi.org/10.1186/s13021-018-0098-0</p> <p>For more information on the methods used to capture TLS data please refer to <a href="https://doi.org/10.1016/j.rse.2017.04.030">Wilkes et al. 2017</a></p> <p>Please acknowldege the producers of this data set if using this data for publication.</p>
Terretrial LiDAR data collected from Malet St, London, UK
<p>Terrestrial LiDAR data collected by the team at University College London.</p> <p>This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices</p> <p><strong>UCL project name</strong>: 2017-02-14.002.riproject</p> <p><strong>Plot ID</strong>: MST</p> <p><strong>State or region</strong>: London</p> <p><strong>Date project started</strong>: 2/14/2017</p> <p><strong>Instrument</strong>: UCL RIEGL VZ-400</p> <p><strong>Scan pattern</strong>: transect along street</p> <p><strong>Angular resolution</strong>: 0.04</p> <p><strong>Images captured</strong>: No</p> <p><strong>Number of scans</strong>: 24.0</p> <p>For more information on the methods used to capture TLS data please refer to <a href="https://doi.org/10.1016/j.rse.2017.04.030">Wilkes et al. 2017</a></p> <p>Please acknowldege the producers of this data set if using this data for publication.</p>
Terretrial LiDAR data collected from Russell Square, London, UK
<p>Terrestrial LiDAR data collected by the team at University College London.</p><p>This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices</p><p><b>UCL project name</b>: 2017-02-08.001.riproject</p><p><b>Plot ID</b>: RSQ</p><p><b>State or region</b>: London</p><p><b>Date project started</b>: 2/8/2017</p><p><b>Instrument</b>: UCL RIEGL VZ-400</p><p><b>Scan pattern</b>: Spiral (11 pos)</p><p><b>Angular resolution</b>: 0.04</p><p><b>Images captured</b>: Yes</p><p><b>Number of scans</b>: 22.0</p><p><b>Google Maps URL</b>: 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</p><p><b>Publications</b>: https://doi.org/10.1186/s13021-018-0098-0</p><p>For more information on the methods used to capture TLS data please refer to <a href="https://doi.org/10.1016/j.rse.2017.04.030">Wilkes et al. 2017</a></p><p>Please acknowldege the producers of this data set if using this data for publication.</p>
LiDAR Data Enrichment By Fusing Spatial and Temporal Adjacent Frames
<p>This is an accompanying video for the manuscript 'LiDAR Data Enrichment By Fusing Spatial and Temporal Adjacent Frames'. This video shows the comparison between the original LiDAR point cloud and the enriched LiDAR point cloud.</p>
Lidar and model data for manuscript submitted to GRL "First Simultaneous Observation of Secondary and Tertiary Gravity Waves by Lidar and Investigation with HIAMCM simulations"
<p>This dataset contains the lidar and model data used in the manuscript submitted GRL titlled '<strong>First Simultaneous Observation of Secondary and Tertiary Gravity Waves by Lidar and Investigation with HIAMCM simulations'</strong></p>
LiDAR-derived voxel-based leaf area density distribution data of urban trees
<p>This dataset includes 3D tree models and radiative transfer codes used in a paper entitled "Simulating the 3D distribution of absorbed shortwave radiation in a tree crown: comparison of simplified and Monte Carlo models" submitted to the JGR: Atmospheres.</p>
UNR Doppler lidar data
<p>University of Nevada, Reno Doppler lidar data files during Langdon Mountain Prescribed Fire on November 7th 2019</p>
Na Lidar Data acquired at Haleakala, Maui, Hawaii from 2002-2005
<p>The dataset contains profiles of temperatures, line-of-sight winds, Na densities, and their uncertainties in Matlab format.</p>
Data set for "Implications of lidar depolarized signal for deformed raindrops remote sensing"
<p>This is the dataset used in the paper "Implications of lidar depolarized signal for deformed raindrops remote sensing"</p>
Data from: Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010
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Data from: An efficient method to exploit LiDAR data in animal ecology
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Data from: Modes of berm and beachface recovery following storm reset: observations using a continuously scanning lidar
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Data from: LiDAR and RGB-image analysis to predict hairy vetch biomass in breeding nurseries
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Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
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International Brain Laboratory public data
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OpenNeuro
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