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68 results for “UAV data”
Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA
We used a DJI Matrice 210 UAV with a MicaSense Altum to collect a total of 20 images from January 2020 - December 2021 in a the Dean Creek marsh on Sapelo Island, GA. Wrack was classified using a principal component analysis. Wrack patches under 1 m2 were excluded from analyses. Wrack classifications were converted to polygon and point data where each point represents a 5 cm x 5 cm pixel. Those files were then used to analyze wrack characteristics, their relation to environmental drivers, and landscape based patterns. For both polygon and point data, we used the National Elevation Dataset (https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/NED.html) to determine the elevation of each wrack patch. Creeks and shorelines were digitized and used to determine each wrack patches' distance to water. We calculated the frequency of wrack deposition at each point by adding together the number of images where that pixel was classified as wrack over the course of the study. Polygon data were related to tide height from a NOAA tidal station data product (Ft. Pulaski, Station 8670870; https://tidesandcurrents.noaa.gov) and wind speed and wind direction from the Marsh Landing weather station (downloaded data for the SAPMLMET met station from: https://cdmo.baruch.sc.edu/) to evaluate the relationship of wrack to environmental drivers.
Fixed-Wing Micro UAV Open Data With Digicam And Raw INS/GNSS - IGN Flight 8
<p>The data set originate from a series of flights conducted with fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications and will also be used in ISPRS workshop on dynamic networks given during the 2021 ISPRS Congress. This is part of a larger series of data that will be released gradually after incorporating user's feedback (e.g., on formats, description,etc.). The data set contains the sensor measurements from GPS, IMU and Camera.</p>
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Data for publication 'Detection of Artificial Seed-like Objects from UAV Imagery'
<p>This resource contains the datasets supporting the model development as published in the article 'Detection of Artificial Seed-like Objects from UAV Imagery' (https://doi.org/10.3390/rs15061637).</p> <p>In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.</p>
UAV-based data for Lake Mulargia (Sardinia, Italy) (2020/09/23)
<p>This dataset contains MicaSense-derived data of Lake Mulargia (Sardinia, Italy) for the 23 September 2020. The acquisition was done by CGR Spa (Italy). Available products are: True-color image (RGB), at-sensor-radiance (TOA), and Bottom-of-atmosphere reflectance data.</p>
Post-remediation evaluation of contaminated site using geophysical methods: Multispectral UAV data Olkusz (Poland) 20220629
<p>In order to analyze the vegetation condition, photos were taken in the infrared (NIR, 750 - 2500 nm) and infrared (Red Edge, 690-720 nm) range. The DJI Matrice 600 platform was used for the raid. The photos were taken from the ceiling of 150 m with the MicaSense Red Edge M camera with a focal length of 6 mm.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
UAV data of post fire dynamics, Quesenbank, Harz, 2022 (orthomosaics, topography, point clouds)
<p>Unoccupied aerial vehicles (UAVs) were used to investigate a burnt forest site situated in the Harz National Park area near the location Schierke, east of the Brocken, called Quesenbank (<a href="https://www.google.com/maps/place/51%C2%B046'07.6%22N+10%C2%B041'30.2%22E/@51.7682386,10.6905312,489m/data=!3m1!1e3!4m5!3m4!1s0x0:0xd8d7f64a3ff9acf1!8m2!3d51.76877!4d10.69173">51.76877 °N, 10.69173 °E</a>) which is a spruce stand stand severely affected by bark-beetle and windfall. Most of the trees are dead such that they provide high fuel loads for potentially occurring wildfires. The small river Wormke crosses the Quesenbank and separates the survey area between a hiking path and the forest stand.</p> <p>During the 12.08.2022, inhabitants reported a <a href="https://www.ndr.de/nachrichten/niedersachsen/braunschweig_harz_goettingen/Waldbrand-im-Harz-Polizei-geht-von-Brandstiftung-aus,waldbrand882.html">fire</a> near Schierke which was quickly contained by the authorities. Two months after the fire, on 13.10.2022, a team of scientists from GAU Göttingen and TU Berlin was accompanied by a National Park representative for investigation. The site was surveyed by using modern UAVs and by sampling soil strata, ash and vegetation. This report will briefly describe UAV-based surveys and the derived data products.</p> <p>For an overview, see the <strong>report </strong>or download the Maps.zip folder.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>Projekt ”Postfeuerdynamik auf Brandflächen im Nationalpark Harz”</strong></p> <p>Dr. Simon Drollinger, Georg-August Universität Göttingen</p> <p>Marlene Düngelhoef, Nationalparkverwaltung Harz</p> <p>Thomas Glinka, Nationalparkverwaltung Harz</p>
FlexiGroBots - Blueberry orchard UAV dataset August 2022 - raw data
<p>This data represents the UAV image acquisition from August 2022 in blueberry orchards located in Babe, Serbia.</p> <p>This is the first part of larger dataset, that contains raw images and orthomosaics generated using these images. Raw images are in 100FPLAN, 101FPLAN, and 102PLAN, while generated orthomosaics are in folder Raw orthomosaics August.</p> <p>Another dataset will be uploaded with preprocessed data titled in the similar manner.</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>
mDRONES4rivers-project: Portfolios of classification results, UAV and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. </p> <p>Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. </p> <p>In this dataset, the following portfolios of classifications, UAS and gyrocopter data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River are available for download:</p> <p>• Multispectral orthophotos produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: MS_ORTHO)</p> <p>• RGB-orthophotos and digital surface models produced with the aid of UAS (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PH_SR_ORTHO_DSM)</p> <p>• Multispectral orthophotos and Digital Surface Models produced with the aid of a gyrocopter (PDF, Detailed description of sensors and data acquisition procedure; abbreviation: PANX_ORTHO_DSM)</p> <p>• Classification results based on UAV- and a gyrocopter data (PDF, Detailed description of processing procedure for different classification levels; abbreviation: CLASSIF_PROD)</p> <p>• German translated version of all above mentioned product portfolios (PDF, abbreviation: product_portfolio_collection_ger)</p>
mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)</p>
Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data
<p>UAV images used for SfM model generation and all images (both SI and OM), in which we measured grain sizes. The code used for image processing and uncertainty estimation of grain size distributions as python files and executable jupyter notebooks, where the latter also serve as documentation.</p>
Spekboom UAV imagery and reference data
<p>Dataset and products for the publication <strong>Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery</strong>.</p>
Replication Data and Analyses for: J. Monsimet, S. Sjögersten, N.J. Sanders, M. Jonsson, J. Olofsson & M. Siewert, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, Remote Sensing in Ecology and Conservation.
<p>This dataset corresponds to the article: <strong>"Jérémy Monsimet*¹, Sofie Sjögersten², Nathan J. Sanders³, Micael Jonsson¹, Johan Olofsson¹, Matthias Siewert¹, 2024. UAV data and deep learning: efficient tools to map the ecological footprint of ants mounds, <em>Remote Sensing in Ecology and Conservation</em>"</strong></p> <p>DOI: <a href="https://doi.org/10.1002/rse2.400" target="_blank" rel="nofollow noreferrer noopener">10.1002/rse2.400</a></p> <p>1 Department of Ecology and Environmental Science, Umeå University, Sweden<br>2 School of Biosciences, University of Nottingham, Loughborough, UK<br>3 Department of Ecology and Evolutionary Biology, University of Michigan, US</p> <p>The gitlab repository of this dataset is available at: <a href="https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/">https://gitlab.com/Monsimet/uav_ants_treeline/-/tree/main/</a></p> <p>In this repository, you will find the analyses and results presented in the paper. In each folder, there is a html file that can be read after downloading locally the whole folder. You can either run the .qmd file used to produce the html file or walk through the html files (see the readme.md for more information).</p> <p>Paper abstract:</p> <p>High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the<em> Betula nana</em> vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.</p>
Hyperspectral Unmixing Dataset of UAV Gathered Blueberry Field Data
<p>Hyperspectral Unmixing dataset created from hyperspectral data gathered usign SPECIM push-broom hyperspectral camera mounted on a UAV flying over blueberry fields in Lithuania. Created dataset contains six data classes and linear mixtures from raw data. All data is given in Python Numpy array .npy files. </p> <p>To keep the annonimity of data owners only the non georectified data cubes are published.</p> <p>Dataset includes three hyperpectral data cubes of blueberry fields and Dark reference cube to show camera noise.</p> <p><strong>Data structure:</strong></p> <p>cube_1, cube_2, cube_2 and Dark - folder with hyperspectral data.</p> <p>calibration_data.npy - Data of calibration plates (with 40%, 10% and 5% reflectance values) from hyperspectral flight that were used to conver DN to reflectance.</p> <p>endmembers.npy - Spectra of siz endmembers (classes) used in the dataset.</p> <p><strong>cube_x folders include:</strong></p> <p>class_matrix.npy - Numpy matrix file of hyperspectral image classes (classification results)</p> <p>raw_data.npy - Hyperspectral cube created from raw camera data (with DN values)</p> <p>data_cube_3_0.npy and abundances_3_0.npy - Classified and mixed (using slidin window of 3x3 pixels with no overlap) hyperspectral data cube and class abundance matrix. </p> <p>endmember_errors.npy - matrix of variation for each of endmembers in the hyperspectral cube (used for evaluation mostly.)</p> <p><strong>Dark folder:</strong></p> <p>includes data folder with raw-dark_fl1_20230830_140006_radiance.dat and .hdr ENVI raster data files (library like <em>rasterio</em> for Python can used to read these files). This is the dark (0% reflectance) data cube and header file used in calibration.</p>
Data and scripts for the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology
<p>This repository contains the modified OpenMMS scripts for Linux and Raspberry Pi firmware for LiDAR sensor presented in the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology at the CAA 2024 conference in Auckland, New Zealand. Included are the LiDAR and trajectory data collected at the site of Antiochia ad Cragum in 2022 in an area roughly north-east of what is known as the Small Bath Area. Each zip file contains two adjacent flights oriented either principally east-west or north-south. The four flights cover the same area in an overlapping pattern.</p> <p>The LiDAR and trajectory data are released under the Creative Commons Attribution 4.0 International license and the modified OpenMMS firmware and scripts are released under the original GNU GPL v3.0 or later license.</p>
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections? - Data Collected and Presented for the Experiments
<p>This dataset contains images collected (and processed) for the experiments of Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?" journal article, a work that defines a new path planning problem for UAVs - the Fast Inspection of Scattered Regions (FISR) - and introduces a novel method that deals with this problem - the multi-UAV Disjoint Areas Inspection (mUDAI) method. For the validation of the introduced methodology, two sets of real-world experiments were executed, one small-scale in Galatsi, Athens, were two mUDAI missions were depolyed, with two different optimization objectives for the data collection procedure (Mazimized Coverage Objective - MCO, and Balanced Coverage Objective - BCO), and one large scale in ZEP-Kissos, Thessaloniki, where a Coverage Path Planning (CPP) mission, and 2 mUDAI missions, one with a single and one with two UAVs, using both the MCO criterion for the data collection, were deployed. Regarding the CPP mission, both the collected images, and the processed results (to generate 2D, 3D, elevation, and plant health maps) are included.</p> <p>In this <a title="mUDAI - ChoosePath platform guide" href="https://sites.google.com/view/mudai-platform/" target="_blank" rel="noopener">page</a> you can find a guide for the on-line platform hosting demo instances of the algorithms used for the deployment of all experiments.</p> <p>In case you use this data, please cite the article:<br>(Article under review - more information to be included soon)</p>
Data from: Flying high: Sampling savanna vegetation with UAV-lidar
<p>The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. <br>To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. <br>Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs, or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimeters or less, which shows great promise for future comparison studies.<br>We discuss how these results apply in practice, explaining the potential trade-offs of flying at higher altitudes and alternating flight pattern. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV-lidar sampling designs in savannas.</p>
IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization
<p>IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization. </p> <p>This data is split up into two folders. </p> <p>"stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer.</p> <p>"UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test. </p>
ScienceDex guides
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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.