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830 results for “LiDAR”
Tree Inventories for Validating Terrestrial Lidar Measurements at Harvard Forest 2007-2014
Our objective is to improve the measurements of canopy structure and biomass of a forest stand and detect their annual changes via a ground-based laser scanning technology, also known as terrestrial lidar (TLS). A TLS instrument utilizes lasers to scan an environment, measure 3D locations of objects encountered by lasers and detect intensities of laser lights scattered by those objects back to the TLS instrument. TLS have shown abilities and is being further explored to retrieve stem diameter, stem count density, stand height, leaf area index, foliage profile, foliage area volume density, aboveground biomass and other useful forest structural parameters rapidly and accurately. Three TLS instruments used in this project include: (1) the Echidna (R) Validation Instrument (EVI), built by CSIRO Australia; (2) Dual-Wavelength Echidna® Lidar (DWEL), built by Boston University, University of Massachusetts, Lowell, University of Massachusetts, Boston and CSIRO Australia; (3) Compact Biomass Lidar (CBL), built by University of Massachusetts, Boston. To validate the forest structural parameters retrieved using these TLS instruments, we set up a one-ha (100 m by 100 m) forest site and collected tree inventory data including: tree location, tree species, DBH, tree height and crown dimension since 2007 with a two-year gap of 2008 and 2009. Lidar data are available from the ORNL DAAC (http://dx.doi.org/10.3334/ORNLDAAC/1045).
Terrestrial LiDAR Scans in the CTFS-ForestGEO Plot at Harvard Forest 2021
In heavily forested and jungle environments where GPS reception is unavailable due to dense canopy cover, it is difficult to determine one's location. Currently, either visual landmarks are used, or open clearings are found where GPS reception can be reestablished. Alternatively, dead-reckoning systems that rely on Inertial Measurement Unit sensor suites can help over moderate distances, but these devices cannot retain positional accuracy over extended ranges. Creare proposes to address this problem by developing the Tree Positioning System. This technological solution will combine a metrology system for determining local tree maps, and geolocalization algorithms that perform spatial pattern matching of local tree maps against a georegistered reference tree map of the area. This system was tested by scanning trees in the ForestGEO plot at Harvard Forest in June 2021.
Canopy LiDAR Measurements in Hemlock Removal Experiment at Harvard Forest 2005
As the ecological functioning of a forest stand is often related to the spatial organization of the canopy, we used a portable canopy LiDAR (PCL; Parker et al. 2004) to measure volumetric canopy structure in the simulated HWA management treatments. In September and October 2005, prior to leaf abscission, we set up 16 parallel transects spaced 2 meters apart in the 900 m2 (30 x 30 m) interior of each of the eight treatment plots. The orientation of the transects were either north-south or east-west to minimize the difficulty of traversing the stand. The distances to canopy surfaces more than1 m above the ground along the transects were recorded with the PCL. Assuming a constant horizontal sampling rate, the continual height measures were binned into 1 m intervals. From these measures, vertical canopy profiles, canopy openness, canopy rugosity, and other metrics related to the three-dimensional structure of the canopy can be derived. These canopy measures will be repeated minimally at 5 and 15 year intervals to develop an understanding of early structural dynamics and micrometeorological consequences associated with the simulated HWA treatments. (Parker, G.G., D.J. Harding, and M.L. Berger. 2004. A portable LIDAR system for rapid determination of forest canopy structure. Journal of Applied Ecology 41, 755-767).
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities
<p><span>The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities and spatial resolutions (i.e., plots of 1 × 1 m, 2 × 2 m, 5 × 5 m and 10 × 10 m size). A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, </span><span><a href="https://doi.org/10.1016/j.dib.2022.108798"><span>https://doi.org/10.1016/j.dib.2022.108798</span></a></span><span>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, </span><span><a href="https://doi.org/10.1016/j.softx.2020.100626"><span>https://doi.org/10.1016/j.softx.2020.100626</span></a></span><span>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m<sup>2</sup> was used. Initially, 100 plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands in Dutch Natura 2000 sites that predominantly contain woodland habitats (using shapefiles from the European Environmental Agency). For each centre point, square polygons of the desired resolutions (i.e., 1 × 1 m, 2 × 2 m, 5 × 5 m or 10 × 10 m plot size) were generated. The square polygons were subsequently used to clip the LiDAR point clouds from the Dutch AHN4 point cloud dataset. Since not all locations of the 100 randomly placed plots contained points, the actual sample sizes were slightly smaller than 100, i.e., 94 plots for the 1 × 1 m, 2 × 2 m and 5 × 5 m resolution and 95 plots for the 10 × 10 m resolution. Metrics were calculated with the original point density of the Dutch AHN4 dataset (20–30 points/m2) and with six systematically down-sampled point clouds for the same plots (i.e., keeping 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds). For each clipped point cloud of a plot at a given resolution, the points were first sorted according to their GPS acquisition time (from earliest to latest). Points were then systematically discarded and only 5%, 10%, 20%, 40%, 60% and 80% of the points in the original point clouds were kept. The kept points were used for calculating the 25 LiDAR vegetation metrics. </span></p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval. Errors of these values are also included. -999 represents missing value. </p>
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 10/30/2016
<p>Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5-km intervals and from 23.7 UT 10/29/2016 to 8.9 UT 10/30/2016 at 0.1-hour intervals. Errors of these values are also included. -999 represents missing values. </p>
Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer
<p>Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1].</p> <p>[1] Brugger P, Fuertes FC, Vahidzadeh M, Markfort CD, Porté-Agel F. Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer. <em>Remote Sensing</em>. 2019; 11(19):2247. https://doi.org/10.3390/rs11192247.</p>
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
<p>Dataset of the paper " Dataset of the paper "Characterization of Wind Turbine Wakes with Nacelle-Mounted Doppler LiDARs and Model Validation in the Presence of Wind Veer" published in Remote Sensing [1]. " published in Wind Energy Science [1].</p> <p>[1] Brugger, P., Markfort, C., and Porté-Agel, F.: Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars, Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, 2022.</p>
PS116-Lidar_Data_Profiles
<p>The lidar profiles are retrieved with Klett or Raman method. Averaged periods are determined with taking into account of the continous cloud-free profiles.</p> <p>Each retrieving result consists of one *.txt and one corresponding *-info.txt file. The filename is structured as {instrument}_{date}_{starttime}-{endtime}-{smooth window}. (UTC is used as the time standard for all the analysis.)</p> <p>*.txt contains the backscatter (extinction) coefficient and some other related results. The *-info.txt contains the retrieving configuations, like retrieving method, reference height and so on.</p>
Costal operating wind farms: two datasets with concurrent SCADA, LiDAR and turbulent fluxes
<p>This data collection consists of two datasets from a micrometeorological experiment conducted in two distinct operating wind farms in a coastal area of the northeast region of Brazil, called Pedra do Sal Wind Farm (UEPS) and Beberibe Wind Farm (UEBB). These wind farms are located on the northeast coast of Brazil where meteorological conditions are strongly influenced by trade winds and sea breeze. Both datasets represent a full-year of measurements from August/2013 to July/2014.</p> <p>On both operating wind farms it was commissioned a fully instrumented IEC-compliant 100m met mast, with five levels of first-class calibrated cup anemometers and one level (100m) with 3D sonic anemometer. Additionally at UEPS there's an extra 3D sonic at 20m height on the met mast, as well as a VAISALA LEOSPHERE Windcube8 doppler wind lidar with a range up to 500m height and located 2.5D upwind of one of the wind turbines.</p> <p>The Pedra do Sal wind farm (UEPS) has an installed capacity of 18MW, with 20 Enercon E-44 installed at 55m a.g.l. At Beberibe wind farm (UEBB) there are 32 Enercon E-48 wind turbines installed at 75m a.g.l. The dataset includes 10min SCADA data for all wind turbines on both wind farms.</p> <p>This dataset has a high-quality combination of meteorological, SCADA and turbulent flux data of two operating wind farms in Brazil. During a full-year of measurements both datasets had a high data recovery rate (see attached tables). The dataset has already been used to assess the impact of atmospheric stability on the wind farm performance, as well as the effect of mesoscale patterns on the wind profile and wind farm power production. Recirculation of the sea breeze and the development of an internal boundary layer upwind the wind turbines were also characterized.</p> <p>For more details on the experimental layout, wind turbine locations, meso and microcale wind conditions and any other information not stated in the NetCDF4 files, please refer to the reference material or contact one of the authors.</p> <p> </p>
Radar and Lidar scattering lookup tables for atmospheric hydrometeors using a T-Matrix method and a Mie theory
<h2>Overview</h2> <p>The database includes text files containing the scattering amplitude matrices for single spherical/nonspherical particles for radar and lidar. They are the lookup tables used for calculating radar and lidar observables in the Cloud-Resolving Radar Simulator (Oue et al. 2020). The radar scattering properties were calculated for several hydrometeor categories using a T-matrix method proposed by Mishchenko (2000) accounting for incident angles, scattering direction (forward and backward), polarimetry (horizontally (H) and vertically (V) polarized waves), particle aspect ratio, phase (liquid or ice), bulk density, temperature, particle size, and radar frequency. The lidar scattering properties at a vertical incidence were calculated for spherical liquid or ice particles using the BHMIE Mie code (Bohrean and Hyffman,1998) accounting for lidar wavelength, temperature, and bulk density. The hydrometeor categories are commonly used for cloud resolving models employing bulk microphysical schemes (e.g., cloud, rain, ice cloud, snow aggregates, and graupel). Detailed descriptions are also available in the CR-SIM user guide (https://github.com/marikooue/CR-SIM/releases/tag/crsim-v3.34).</p> <h2>Data structure</h2> <p>The data files are arranged and zipped every hydrometeor types. The names of the tar-zipped directories under the top directory LLUT3 represents the hydrometer type.<br>For lidar scattering, the following directories are included:<br>ceilo: Ceilometer lidar backscatter properties at a wavelength of 905 nm<br>mpl: Micropulse lidar (MPL) backscatter properties at wavelengths of 353 and 532 nm</p> <p>For radar scattering, the following hydrometer types are included:<br>cloud: Radar scattering for liquid cloud droplets (spherical shape)<br>raina: Radar scattering for raindrops with the aspect ratio model proposed by Andsager et al. (1999)<br>rainb: Radar scattering for raindrops with the aspect ratio model proposed by Brandes et al (2002)<br>ice_ar0.90: Radar scattering for cloud ice with an aspect ratio of 0.9<br>ice_ar0.20: Radar scattering for cloud ice with an aspect ratio of 0.2<br>smallice: Radar scattering for spherical cloud ice particles<br>snow_ar0.60: Radar scattering for snowflakes with an aspect ratio of 0.6<br>graupel_ar0.60: Radar scattering for graupel particles with an aspect ratio of 0.6<br>graupel_ar0.80: Radar scattering for graupel particles with an aspect ratio of 0.8<br>graupel: Radar scattering for spherical graupel particles<br>gh_ryzh: Radar scattering for graupel particles with the graupel aspect ratio model proposed by Ryzhkov et al (2011)<br>unrimedice_ar0.40: Radar scattering for unrimed ice particles with an aspect ratio of 0.4<br>unrimedice_ar0.60: Radar scattering for unrimed ice particles with an aspect ratio of 0.6<br>unrimedice_ar0.80: Radar scattering for unrimed ice particles with an aspect ratio of 0.8<br>unrimedice: Radar scattering for spherical unrimed ice particles<br>partrimedice_ar0.40: Radar scattering for partially rimed ice particles with an aspect ratio of 0.4<br>partrimedice_ar0.60: Radar scattering for partially rimed ice particles with an aspect ratio of 0.6<br>partrimedice_ar0.80: Radar scattering for partially rimed ice particles with an aspect ratio of 0.8<br>partrimedice: Radar scattering for partially rimed spherical ice particles </p> <h2>The file name convention </h2> <p>For lidar scattering data, each file name has the following format:<br>[hydrometeor type]_[instrument name]_ [wavelength in nm]_[phase ID]_d[bulk density in kg m-3].dat<br>The hydrometeor type shows: 1) ‘cld’ for liquid cloud droplets, and 2) ‘ice’ for ice particles. The phase ID shows: 1) ‘p25’ for ceilometer liquid cloud, 2) ‘p20’ for MPL lidar liquid cloud, and 3) ‘m30’ for MPL lidar ice. </p> <p>For radar scattering data, each file name has the following format.<br>[hydrometeor type]_fr[frequency in GHz]GHz_t[temperature in K]_rho[bulk density in kg m-3]_el[elevation angle in degree].dat<br>The hydrometeor type follows the directory name presented above.</p> <h2>Format of the data files</h2> <p>Line 1: Wavelength in mm<br>Line 2: Temperature in K<br>Line 3: Refractive index (real and imaginary)<br>Line 4: Number of radii calculated and number of elevation angles<br>Line 6: Incident angle and scattered angle in degrees<br>Line 7: Radius in mm and aspect ratio<br>Line 8: Forward scattering amplitude for co-polarization VV and HH (complex number)<br>Line 9: Backward scattering amplitude for co- and cross polarizations VV, VH, HV, HH (complex number) <br>Line 10 to the end of file: Repeat Line 7 to Line 9 with different radii until the maximum radius.</p>
Hong Kong Annotated Airborne LiDAR Point Clouds
<p>The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds <sup>[1]</sup>. The dataset covers 16 tiles of airborne LiDAR data in an area of 7.2 km2 in Shatin, Hong Kong, China. 11 tiles were used for training, while 5 tiles were used for validation. There are multiple types of construction in the dataset including high-rise residential buildings, low-rise village houses, and large public buildings. Green spaces are mainly composed of wood areas in open spaces (e.g., in parks and hills) and planted trees in residential gardens and nearby roads. Point clouds are classified in ground, buildings, and trees.</p> <p>The LiDAR data is owned by the Hong Kong government. Please visit the Spatial Data Portal, Survey Division, CEDD (https://sdportal.cedd.gov.hk/#/en/) for more details.</p> <p> </p>
LIDAROC dataset 10m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 10m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div> </div> <div> </div>
LIDAROC dataset 5m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <p>This dataset is the 5m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 10m and 20m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> <div><a title="LIDAROC 20m" href="../records/12800632">LIDAROC 20m</a></div>
LIDAROC dataset 20m: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability.
<p>Keywords: LiDAR Point Cloud corruption, Sensor phenomena, anomaly, autonomous vehicle, contamination, dataset, object detection benchmark, perception robustness testing, sensor.</p> <div>LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR samples.</div> <div> </div> <div> <div> <p>This dataset is the 20m dataset, which is part of the larger LIDAROC dataset.</p> <p>The experiment was conducted in two environments: The first was a subterranean narrow hallway with the target approximately 5 meters away, referred to as the 5m dataset, simulating a complex urban driving scenario. The second environment was a spacious outdoor area with two distance variations (10 and 20 meters).</p> <p>For the 5m and 10m datasets, please refer to the link below:</p> </div> <div><a title="LIDAROC 5m" href="../records/12800039">LIDAROC 5m</a></div> <div><a title="LIDAROC 10m" href="../records/12800559">LIDAROC 10m</a></div> </div> <div> </div>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.