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38 results for “unmanned aerial vehicles”
Landscape Phenology from Unmanned Aerial Vehicle Photography at Harvard Forest 2013
This data set contains orthophotos in the vicinity of the EMS tower at Harvard Forest, as well as the flight logs from the unmanned aerial vehicle (UAV) used to obtain the digital images used in orthophoto creation. Orthophotos were created by mosaicking approximately 200 JPEG images from each date of observation. The orthophotos cover the spatial extent of the 250 meter resolution MODIS pixel that contains the EMS tower. Land cover types in the area of photography include deciduous and evergreen forest, and wetlands. The research goal of data collection for this data set was to observe spatial variance in plant phenology. Therefore, photos were taken from before leaf out until after leaf drop. Orthophotos were collected approximately every 5 days during spring and weekly during fall; see filenames for specific dates. The nominal spatial resolution of the orthophotos is 6 cm, however due to various factors including inaccuracy of the onboard GPS, wind-blown motion of trees, the automated orthophoto mosaicking process, and user error in final georeferencing, image analysis has been conducted at 10 m resolution. The orthophotos are available as GeoTIFF files.
Unmanned Aerial Vehicles Dataset
<p><strong>Unmanned Aerial Vehicles Dataset:</strong></p> <p>The Unmanned Aerial Vehicle (UAV) Image Dataset consists of a collection of images containing UAVs, along with object annotations for the UAVs found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for UAVs. The dataset is intended for use in research and development of UAV-related applications, such as autonomous flight, collision avoidance and rogue drone tracking and following. The dataset consists of the following images and detection objects (Drone):</p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Drone</td> </tr> <tr> <td>Training</td> <td>768</td> <td>818</td> </tr> <tr> <td>Validation</td> <td>384</td> <td>402</td> </tr> <tr> <td>Testing</td> <td>383</td> <td>400</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p> <p> </p> <p><strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong></p> <blockquote> <p>Rafael Makrigiorgis, Nicolas Souli, & Panayiotis Kolios. (2022). Unmanned Aerial Vehicles Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7477569</p> </blockquote>
Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D reconstruction (UAVID3D)
<p>Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However, these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area. </p> <p>To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository. </p> <p>RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition, thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and between 58 and 65 degree fahrenheit respectively. </p> <p>For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images, similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories <a href="https://github.com/LBNL-ETA/a3dbr">https://github.com/LBNL-ETA/a3dbr</a>, and <a href="https://github.com/LBNL-ETA/AutoBFE">https://github.com/LBNL-ETA/AutoBFE</a></p> <p>This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. </p> <p> </p> <p> </p>
Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil
<p>Title:</p> <p>Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170711</p> <p>Time of day (BRT = -3)</p> <p>10h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil
<p>Title:Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High-speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil</p> <p> </p> <p>Data description:</p> <p> </p> <p>The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p> </p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20170707</p> <p>Time of day (BRT = -3)</p> <p>14h a.m.</p> <p>UAV – Drone - Camera</p> <p>Phantom 4.</p> <p>Fly high (meters above ground)</p> <p>250 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>143</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p>
Dataset used in "Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle". https://doi.org/10.5194/hess-2017-625.
<p>Dataset used in</p> <p>Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle</p> <p>Filippo Bandini<sup>1</sup>, Daniel Olesen<sup>2</sup>, Jakob Jakobsen<sup>2</sup>, Cecile Marie Margaretha Kittel<sup>1</sup>, Sheng Wang<sup>1</sup>, Monica Garcia<sup>1</sup>, and Peter Bauer-Gottwein<sup>1</sup></p> <ul> <li><sup>1</sup>Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark</li> <li><sup>2</sup>National Space Institute, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark</li> </ul> <p><strong>Hydrol. Earth Syst. Sci.</strong></p> <p><strong>https://doi.org/10.5194/hess-2017-625</strong></p> <p> </p> <p>The dataset contains</p> <p>-data/observations that were used to obtain the figures shown in the paper. Data have .mat extension (Binary data container format used by MATLAB; may include arrays, variables, functions, and other types of data;)</p> <p>-scripts to compute statistics and plot data, with .m extension (contain MATLAB code, either in the form of a script or a function)</p> <p>-shape files (shp — shape format; the feature geometry itself, .shx — shape index format, .dbf — attribute format, .prj — projection format; .sbn and .sbx — spatial index of the features, .cpg — used to specify the code page, .<em>qpj</em> QGIS projection file) or raster files (.geotiff) to reproduce the map contents reported in the referenced paper.</p> <p>The repository is subdivided into directories containing the dataset shown in the paper. These directories are named with the figures and/or tables numbers of the referenced paper. </p>
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments [dataset]
<p>This dataset is related to "Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments" in IEEE RA-L,2019.</p> <p> </p> <p>Data Capture<br> ========================<br> Data is obtained by manually flying the UAV through the redwood forest environment using a FrSky Taranis (Plus) Digital Telemetry Radio System. In total, 81,674 frames were captured together with the flight behaviour that comprehends flights under and above the forest canopy, navigation inside caves and on river beds, lakes and mountains.</p> <p> </p> <p>Folder Structure<br> ========================<br> |-manual_0 - manual_5: sequences containing training data</p> <p>|-test_0 - sequences containing testing data</p> <p> </p> <p>Data Protection<br> ========================<br> Gathered by simulated flight using Microsoft AirSim (2019) and released in accordance with MSR Aerial Information and Robotics Simulator (AirSim) lisence, which is described in details bellow:</p> <p> </p> <blockquote> <p>The MIT License (MIT)</p> <p>MSR Aerial Informatics and Robotics Platform<br> MSR Aerial Informatics and Robotics Simulator (AirSim)<br> Copyright (c) Microsoft Corporation<br> All rights reserved.<br> MIT License</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the ""Software""), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:<br> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.<br> THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p> </blockquote>
Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)
<p>We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower. </p>
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
<p>Add citation file.</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: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
<p>This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.</p>
Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)
<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>
Bridge Inspecting with Unmanned Aerial Vehicles R&D
<p>Corresponding data set for Tran-SET Project No. 17STLSU11. Abstract of the final report is stated below for reference:</p> <p>"The project achieves through research including literature, on site interviews, and experimentation: 1) a recommendation for a UAV-based system to practically assist in routine bridge inspection work in the State of Louisiana, 2) the identification and description of advantages, disadvantages, and limitations in the use of UAVs for routing bridge inspection work in Louisiana, and 3) provided recommendations for future work. The Yuneec H520 aircraft and its E90 camera are recommended, as is the need for a boat to be included as part of the system. The recommended system has advantages in reaching portions of the bridge that are difficult to reach by human inspectors and includes sufficient image resolution to assist the bridge inspection process. A disadvantage though, is that of the overburden of regulations both from the FAA and for getting permission to inspect a bridge using a UAV. These regulations my render negligible, any gains in efficiency perceived in the use of UAVs for bridge inspection. Also, the UAV is described by the project as an assistance tool for the manual bridge inspection process and cannot replace the needed work of bridge inspectors, as it has limitations. For example, the UAV cannot perform inspections beneath the bridge deck since it may lose its GPS navigation reference. Likewise, it cannot see beneath the surface to tell of concrete components have subsurface cracks or timbers might be hollow. These tests are still the domain of manual bridge inspection. The project provided recommendations with respect to changes in how inspections should be done using the UAV, i.e. in the pre-inspection phase, needed field studies using the UAV, needed economics alternative-tradeoffs studies, and recommendations for augmenting the aircraft and its instruments. The Second phase, i.e. the Implementation Phase, will utilize the information and educational fruits of the technical research phase for tutorials, seminars and to facilitate feedback surveys with engineering firms, the LADOTD, engineering societies, and students."</p>
High resolution LiDAR dataset acquired using UAV (unmanned aerial vehicle) over two vineyards and two years located in 'Tomiño', Pontevedra, Spain.
<p>This dataset features an extensive collection of LiDAR data from vineyards in northern Spain, targeting vineyards to address the growing demand for public UAV LiDAR datasets in Agricultural Sciences. The data was gathered using a DJI M300 multi-rotor platform equipped with a DJI Zenmuse L1 LiDAR sensor, conducting UAV flights at 20, 30, and 50 meters Above Ground Level (AGL) across two vineyards during 2021 and 2022. The dataset comprises ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. This information is essential for studying vineyard morphology and development and plays a key role in refining vineyard management tactics. In addition, the dataset is valuable for agricultural robotics, providing detailed terrain and canopy data crucial for designing efficient flight paths and navigation algorithms. Finally, it serves as a true "ground truth" dataset to verify satellite-derived models, enabling the generation of high-precision digital elevation models (DEMs) and other derivatives.</p>
The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions - dataset
<p>This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:</p> <ol> <li>Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where: <ul> <li>[location] is the place of data collection (e.g., Cocora, Vinicucna)</li> <li>[year] is the year of data collection (e.g., 2023)</li> <li>[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade</li> <li>[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)</li> </ul> </li> <li> <p>Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).</p> </li> </ol> <p>Structure-from-motio<span> </span>n processing was performed in Agisoft Metashape (<a href="https://www.agisoft.com/">https://www.agisoft.com/</a>, Agisoft, 2023). Mapping was performed in ArcGIS Pro (<a href="https://www.esri.com/en-us/arcgis/about-arcgis/overview">https://www.esri.com/en-us/arcgis/about-arcgis/overview</a>, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).</p> <p>Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. <em>International Journal of Applied Earth Observations and Geoinformation</em>, 103474. doi:<a href="https://doi.org/10.1016/j.jag.2023.103474"> https://doi.org/10.1016/j.jag.2023.103474</a></p>
Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
Open the record for dataset details and reuse information.
Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
Open the record for dataset details and reuse information.
Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil
<p>Fábio Marcelo Breunig¹ (author)</p> <p><em>¹</em> <em>Universidade Federal de Santa Maria, Departamento de Engenharia Florestal, Frederico Westphalen, Rio Grande do Sul, Brasil. </em><em>E-mail: </em><em>breunig@ufsm.br</em></p> <p>Title:</p> <p> </p> <p>Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil</p> <p>Data description:</p> <p> </p> <p><br> The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also <em>Drone</em>) covering an experimental area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.</p> <p> </p> <p>Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.</p> <p>UAV and camera settings for the acquisition (Specifications Table):</p> <p> </p> <p><strong>Parameters</strong></p> <p><strong>Specification/value</strong></p> <p>Date (YYYYMMDD):</p> <p>20201020</p> <p>Time of day (BRT = -3)</p> <p>11 h a.m.</p> <p>UAV – Drone - Camera</p> <p>Matrice 100 X3</p> <p>Fly high (meters above ground)</p> <p>80 m</p> <p>View angle</p> <p>90° automatic mode.</p> <p>Sky conditions</p> <p>( x ) Clear sky</p> <p>( ) Low cloud coverage (some clouds)</p> <p>( ) Completely cloudy</p> <p>Wind condition</p> <p>( x ) no wind</p> <p>( ) Low speed</p> <p>( ) High speed wind</p> <p>Approximate data acquisition duration</p> <p>16 minutes</p> <p>Total of photographs acquired</p> <p>224</p> <p>Across track coverage</p> <p>80%</p> <p>Cross-track coverage</p> <p>80%</p> <p>Fly planning software</p> <p>Pix4D Capture</p> <p> </p> <p>For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a></p> <p>An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.</p> <p> </p> <p>Figure 2. The capture of an orthomosaic and processing workflow</p> <p> </p> <p> </p> <p>References to the main project/publications:</p> <p> </p> <p>Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.</p> <p> </p> <p>Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0</p> <p> </p> <p>Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8</p> <p> </p> <p>Acknowledgments:</p> <p> </p> <p>This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Grant 23830.388.22048.19092016).</p> <p> </p> <p>Other considerations</p> <p> </p> <p>PS. A pdf file is also attached with this description</p> <p> </p> <p>Declaration of Competing Interest</p> <p> </p> <p>The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.</p> <p> </p> <p>References associated:</p> <p> </p> <p>Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943</p> <p>Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, <em>Meteorologische Zeitschrift</em>, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507></p> <p>Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897548</p> <p>Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria,<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.897909</p> <p>Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil.<em> PANGAEA</em>, https://doi.org/10.1594/PANGAEA.910114</p> <p>Breunig, Fábio Marcelo (2017, July 11). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4328340</p>
Data from: A view from above: A view from above: unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies
1. Tropical forests store and sequester large quantities of carbon, mitigating climate change. Lianas (woody vines) are important tropical forest components, most conspicuous in the canopy. Lianas reduce forest carbon uptake and their recent increase may, therefore, limit forest carbon storage with global consequences for climate change. Liana infestation of tree crowns is traditionally assessed from the ground, which is labour-intensive and difficult, particularly for upper canopy layers. 2. We used a light-weight unmanned aerial vehicle (UAV) to assess liana infestation of tree canopies from above. We used a commercially available quadcopter UAV with an integrated, standard three-waveband camera to collect aerial image data for 150ha of tropical forest canopy. By visually interpreting the images, we assessed the degree of liana infestation for 14.15ha of forest for which ground-based estimates were collected simultaneously. We compared the UAV liana infestation estimates with those from the ground to determine the validity, strengths and weaknesses of using UAVs as a new method for assessing liana infestation of tree canopies. 3. Estimates of liana infestation from UAV correlated strongly with ground-based surveys at individual tree and plot level, and across multiple forest types and spatial resolutions, improving liana infestation assessment for upper canopy layers. Importantly, UAV-based surveys, including the image collection, processing and visual interpretation, were considerably faster and more cost-efficient than ground-based surveys. 4. Synthesis and applications: UAV image data of tree canopies can be easily captured and used to assess liana infestation at least as accurately as traditional ground data. This novel method promotes reproducibility of results and quality control, and enables additional variables to be derived from the image data. It is more cost-effective, time-efficient and covers larger geographical extents than traditional ground surveys, enabling more comprehensive monitoring of changes in liana infestation over space and time. This is important for assessing liana impacts on the global carbon balance, and particularly useful for forest management where knowledge of the location and change in liana infestation can be used for tailored, targeted and effective management of tropical forests for enhanced carbon sequestration (e.g. REDD+ projects), timber concessions and forest restoration.14-Nov-2018
Data set - Monitoring light pollution with an unmanned aerial vehicle
<p>Dataset used in the study Monitoring light pollution with an unmanned aerial vehicle. A digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken contemporarily during flights at 70 meter and 100 meters altitude, and also varying exposure time.</p>
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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.