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15 results for “unmanned aerial systems”
University of Nebraska-Lincoln Unmanned Aerial System observations from LAPSE-RATE
<p>This dataset contains thermodynamic (pressure, temperature, humidity) measurements from the University of Nebraska-Lincoln unmanned aerial system multirotors during the LAPSE-RATE campaign from 14--19 July 2018. This dataset contains 171 files from two multirotors. File names are coded as per the LAPSE-RATE community standard naming agreement. See README_v1.txt for more information about the naming convention and other details.</p>
Unmanned aerial system data of Lirung Glacier and Langtang Glacier for 2013–2018
<p>This dataset contains the raw data as well as produced image mosaics, digital elevation models (DEMs) and data derivatives of optical (RGB) unmanned aerial vehicle surveys of the debris-covered Lirung Glacier (9 surveys, 2013–2018) and Langtang Glacier (7 surveys, 2014–2018) in the <a href="https://www.google.com/maps/@28.2525566,85.6197635,32922m/data=!3m1!1e3">Langtang Catchment</a>, Nepalese Himalaya.</p> <p>All data in this dataset are stored in tape archive (<code>tar</code>) or gzip-compressed tape archive (<code>tar.gz</code>) formats and require extraction before use.</p> <p>The projected coordinate system used in this dataset is <em>WGS 1984 UTM Zone 45N (EPSG:32645)</em>. Survey dates are always provided as <em>yyyymmdd</em>.</p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li><strong><code>dems_<glacier>.tar</code></strong><br>20 cm resolution DEMs that were derived from the raw UAV images. DEMs of all survey dates are included in the tar archives. File format is GeoTIFF.<br> </li> <li><strong><code>orthomosaics_<glacier>.tar</code></strong><br>10 cm resolution image mosaics of orthorectified source imagery (orthomosaics) that were derived from the raw UAV images. Orthomosaics of all survey dates are included in the tar archives. File format is GeoTIFF.<br> </li> <li><strong><code>point-clouds_<glacier>.tar.gz</code></strong><br>Raw dense point clouds that were derived from the raw UAV images. Point clouds of all survey dates are included in the tar archive. File format is ASPRS LAS. Note that additional gzip-compression has been applied to the archives.<br> </li> <li><strong><code>raw-data_<glacier>_<datestamp>.tar</code></strong><br>Raw data of each of the surveys that were performed over 2013–2018. The filename includes the glacier name and the survey date. Each tar archive contains directories for each UAV flight associated to that specific survey, indicated by <em>f1, f2, ..., fn</em>. The flight directories have the following contents: <ul> <li><code>img</code><br>Subdirectory that contains the individual images in JPEG format captured by the UAV camera.</li> <li><code>*flight_path.kml</code> (not present for all surveys)<br>Keyhole Markup Language file that contains the flight path of the UAV recorded by the UAV's internal GPS+GLONASS sensor.</li> <li><code>*image_geoinfo.txt</code><br>Table with coordinates (<em>x,y,z</em>) and UAV orientation (<em>roll, tilt, yaw</em>) for every image in <code>img</code>, which were recorded by the UAV's internal GPS+GLONASS sensor and gyroscope, respectively.</li> <li><code>*drone_log.bbx</code> or <code>*drone_log.bb3</code><br>Binary flight log file from the UAV containing detailed flight information. Can be read by the proprietary eMotion software by UAV manufacturer <a href="https://www.sensefly.com/">senseFly</a>.<br> </li> </ul> </li> <li><strong><code>supplementary-animation_<glacier>.gif</code></strong><br>Animations of Langtang Glacier (2014–2018) and Lirung Glacier (2013–2017) supplementary to Kraaijenbrink and Immerzeel (2025). The high resolution time lapse animations are constructed from composites of the orthomosaic and hillshaded DEM. Since the animations are in GIF format, they are best viewed in a web browser in which they can be zoomed and panned.</li> <li><strong><code>supplementary-animation_lirung_terminus_retreat.mp4<br></code></strong>Three-dimensional fly-by video animation of the terminus retreat of Lirung Glacier (2013–2017), supplementary to Kraaijenbrink and Immerzeel (2025).<br> </li> <li><strong><code>supplementary-data-to-article.tar</code></strong><br>Data derivatives as presented in Kraaijenbrink & Immerzeel (2024). The tar archive contains a README file with additional information for each of the datasets present in the archive. The archive contains: <ul> <li>Error measurements of the UAV product</li> <li>Vector outlines of the area of interests of both glaciers</li> <li>Point cloud extracts of supraglacial ice cliff cross profiles</li> <li>Flow and gradient corrected DEMs (1 m resolution)</li> <li>Pixel-wise regression of the uncorrected and flow-corrected DEMs (1 m resolution)</li> <li>Surface velocity between survey pairs (8 m resolution)</li> </ul> </li> </ul> <p> </p> <p><strong>Reference</strong></p> <p>For further information, e.g. about the UAV systems and cameras used, as well as detailed descriptions of the data and the applied data processing please refer to the accompanying journal article.</p> <p>Kraaijenbrink, P. D. A., & Immerzeel, W. W. (2025). Spatial and temporal variability of the surface mass balance of debris‐covered glacier tongues. Journal of Geophysical Research: Earth Surface, 130, e2024JF007935. <a href="https://doi.org/10.1029/2024JF007935" target="_blank" rel="noopener">https://doi.org/10.1029/2024JF007935</a></p> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).<br>(<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)</p> <p> </p> <p><strong>Correspondence</strong></p> <p>Dr Philip Kraaijenbrink (<a href="mailto:p.d.a.kraaijenbrink@uu.nl">p.d.a.kraaijenbrink@uu.nl</a>)<br>Prof Dr Walter Immerzeel (<a href="mailto:w.w.immerzeel@uu.nl">w.w.immerzeel@uu.nl</a>)</p> <p> </p> <p> </p>
Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
<p>This dataset comprises of the IDL code referenced in the 'Open Research' section of the Kaye and Pittman (2020) study 'Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system' published in <em>Methods in Ecology and Evolution</em> (<a href="https://doi.org/10.1111/2041-210X.13402">https://doi.org/10.1111/2041-210X.13402</a>).</p> <p>This study describes a proof‐of‐concept autonomous unmanned aerial vehicle (UAV) system that utilizes the fluorescence characteristics unique to different materials to scan and acquire targets in the field e.g. fossils, rocks and minerals, organisms and archaeological artefacts. This is possible because these targets are often highly fluorescent against lower fluorescence backgrounds and may exhibit different colours. Fluorescence is stimulated by a near‐UV laser that is projected across the ground as a horizontal line directly below the UAV. The IDL code is for laser line and colour extractions in the laser scan strip. The raw .jpeg data for the IDL code is not provided here as this depends on what target is being scanned. All image data are made available in the paper. Additional contextual information is provided in the '2 MATERIALS AND METHODS' section of the paper, especially in Figure 3.</p>
Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales
1.Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine-scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field-based habitat quality measurements and lower spatial resolution, higher spatial extent satellite-based remote sensing. Our goal in this study was to evaluate the potential for UAS structure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush-steppe environment. 2.At the plant and patch scales, we compared UAS-derived estimates of vegetation height, volume (estimate of food availability), and canopy cover to estimates from ground-based terrestrial laser scanning (TLS), and field-based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho, USA, using point clouds derived from UAS SfM. 3.At the individual plant scale, the UAS-derived estimates matched those from TLS for height (r2 = 0.85), volume (r2 = 0.94), and canopy cover (r2 = 0.68). However, there was less agreement with field-based measurements of height (r2 = 0.67), volume (r2 = 0.31), and canopy cover (r2 = 0.29). At the patch scale, UAS-derived estimates provided a better fit to field-based measurements (r2 = 0.51-0.78) than at the plant scale. Landscape-scale maps created from UAS were able to distinguish structural heterogeneity between key patch types. 4.Our work demonstrates that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine-resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation, and land management.
Quantifying pine processionary moth defoliation in a pine-oak mixed forest using unmanned aerial systems and multispectral imagery (dataset, paper published in PLOS ONE)
<p>Data processed to analyze pine processionary moth defoliation.</p> <p>Digital surface model and orthomosaics derived from UAS</p>
Increasing Bridge Durability and Service Life with LIDAR Enhanced Unmanned Aerial Systems (UAS)
<p>Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In the future, if it can be automatized, it can be implemented to simplify the complexity of inspections. The authors developed a platform to mount the camera, sonar laser, and DAQ on the UAS and remotely controlled the data collection operation. Additionally, an algorithm was developed which can automatically obtain the geometric information of the rebar. The proposed automated RGBD-equipped UAS system was developed, fabricated, and tested in the Balloon Fiesta Park on a simulated bridge deck at different heights and with different UAS motions to obtain the best distance, speed, and motion for real construction field. The authors also conducted an outdoor experiment in a construction field at White Rock to validate the capability of the proposed system on the real site with vertical rebar and the challenges of the real construction site. The result confirmed that the LIDAR-equipped UAS system has the potential to help the inspection process in terms of time, accuracy, safety, and generating a permanent record of the inspection. Bridge construction information collected by LiDAR-equipped UAS technology can eventually provide bridge managers with transparent condition assessment and one-step decision-making support through quantitative measurement combined with 3D visualization to facilitate repair planning that can greatly facilitate maintenance.</p>
Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
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Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales
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Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
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Data from: Terrestrial mammalian wildlife responses to Unmanned Aerial Systems approaches
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Dataset used in "Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: comparison of radar altimetry, LIDAR and photogrammetry techniques"
<p>Dataset for research paper "Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: comparison of radar altimetry, LIDAR and photogrammetry techniques", published in Remote Sensing of Environment 2019, Elsevier Journal.</p> <p>Dataset includes:</p> <p>-MATLAB_codes.rar: zip file that contains MATLAB codes. MAIN.m is the main code, it refers to external functions that are included in the zip file. MAIN.m plots the figures of the paper in which we compare radar, LIDAR and photogrammetry and computes statistics of table 3 (table of the paper)</p> <p>-zip file WL_observations_Aomose.zip contains LIDAR, radar, and photogrammetry observations to be loaded by MATLAB code MAIN.m</p> <p>-the LIDAR Digital Surface Model (DSM_final.tif) retrieved in the stream Amose Å</p> <p>-the photogrammetry Digital Elevation Model (DEM_nov.tif) and orthomosaic (orthomosaic_nov.tif) </p> <p> </p>
Dataset related to the publication "The Use of Unmanned Aerial Systems to Map Intertidal Sediment," Remote Sensing, 2018
<p>This upload contains data related to the publication 'The Use of Unmanned Aerial Systems to Map Intertidal Sediment' by Fairley, I., Mendzil, A., Togneri, M., Reeve, D.E., Remote Sensing, 2018, accepted.<br> Any use of the data should cite the above publication.</p> <p>The data was collected through the DST-UAV project, funded by the UK NERC, project reference NE/R014485/1</p> <p>The .zip folder data is organised in subfolders for each flight date. Each subfolder is named in the following way 'siteName-ddmmyy.'<br> Within each subfolder are RGB orthomosaics and multispectral reflectance maps as geotiffs.</p>
A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles
<p>Datasets collected from the experiments described in the paper R. Milijas, L. Markovic, A. Ivanovic, F. Petric and S. Bogdan, "A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles," <em>2021 International Conference on Unmanned Aircraft Systems (ICUAS)</em>, 2021, pp. 1148-1154, doi: 10.1109/ICUAS51884.2021.9476802.</p> <p>The datasets consist of ROS bags which hold the UAV and LiDAR data, and of zip files which hold only the lidar data in binary format for non-ROS users.</p>
Automated unmanned aerial system for camera-based semi-automatic triage categorization in mass casualty incidents - Minimal dataset
<p>Minimal dataset for automated UAS based triage.</p>
Aviation Safety Reporting System: Unmanned Aerial Vehicle (UAV) Reports
A sampling of reports involving Unmanned Aerial Vehicle (UAV) events.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.