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401 results for “UAVs”
Figure 5. Images binarized by SVM.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>In instance, the image in Fig. 4 was binarized manually (however, the person in charge of<br> color adjustment is professional) and by using SVM resulting in Fig.5</p>
Figure 4. Images binarized by hand.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>In instance, the image in Fig. 4 was binarized manually (however, the person in charge of<br> color adjustment is professional) and by using SVM resulting in Fig.5</p>
Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System-Figure 3. The principle of SVM
<p>Manual segmentation training was hard and very time consuming and rely to operator's<br> accuracy, so we developed a color calibration algorithm using SVM(Support Vector Machine). In<br> this subsection, we present a color recognition algorithm using the support vector machine<br> (SVM).SVM is one of the classification algorithms which it has high generality since it can<br> calculate a super plane that maximizes the margin of classes, Fig.3.[8] In our algorithm, the SVM is<br> trained by the H'SY values of the classes and the mean of the obtained image H'SY values. After<br> training, the obtained image is binarized by setting the maximum and minimum value in the<br> distribution of each class as a threshold.</p>
Figure 2. Catadioptric projection modelled by the unit sphere-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the beginning of UAV, the map building was one of the most addressed problems by<br> researchers. Several researchers used Omni directional vision for robot navigation and map<br> building. Because of the wide field of view in Omni directional sensors, the robot does not need to<br> look around using moving parts (cameras or mirrors) or turning the moving parts. The global view<br> offered by Omni directional vision is especially suitable for highly dynamic environments. The<br> Omni directional vision system consists of a hyperbolic mirror, a USB color digital camera<br> (Logitech C905) and a regulation device.</p>
Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Visual servoing is an approach to control motion of a helicopter using information feedback<br> from a camera mounted on it. For their tremendous potential applications in various areas including<br> environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively<br> studied in robotics and control in recent years. However, the research advance in dynamic control of<br> small helicopters is limited due to highly coupled non- linear dynamics and the existence of various<br> uncertain- ties. Many people studied controller design based on a Publisher Item Identifier.<br> linearized or simplified model, but the controllers developed under linearized models cannot<br> guarantee dynamic stability rigorously. Another effort is application of modern non-linear control<br> theory to helicopter control because small helicopter are good test beds for sophisticated control<br> techniques for their small size and highly coupled dynamics [4].</p>
UAV-derived cluster greenness and pathlength of individual trees collected at Marden Park, UK
<p>Inidivual tree cluster greenness (gcc) and pathlength used in the study "UAV-derived greenness and within-crown spatial patterning can detect ash dieback in individual trees".</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>
UAV Railroad Images for Segmentation and Obstacle Detection
<p><span>UAV-RSOD dataset contains five different types of obstacles including </span><span>person, boulder, barrel, branch, jerry can, and iron rod. This dataset supports the development and evaluation of computer vision models for railroad-related applications, such as autonomous train navigation, obstacle detection in railroad, railroad extraction and safety monitoring. </span><span> </span><span>The DJI Phantom 4 PRO UAV </span><span>Drone was used to take all the videos and images in the Indian Railways, Tiruchirapalli city of Tamilnadu, India.</span></p> <p><span>Version 1: 315 Images with Labelling, Masking for Segmentation Process and without any Data Augmentation techniques applied</span></p> <p><span>Version 2: 2002 Annotated Images with Data Augmentation techniques applied (Rotate, Flip, etc) for Obstacle Detection</span></p>
AdM_UAV
<p>This dataset contains two set of images (trajectories) taken by a UAV equipped with a downward looking camera. The region in which the UAV flyed was Arroio do Meio, a city from Rio Grande do Sul, Brazil. In addition to the sets of images, there are also a folder with five satellite images from Arroio do Meio and a README file. </p> <p> </p> <p>This dataset was used in the experiments of: </p> <p>M. Mantelli, D. Pittol, R. Neuland, A. Ribacki, R. Maffei, V. Jorge, E. Prestes, M. Kolberg, "A novel measurement model based on abBRIEF for global localization of a UAV over satellite images."</p> <p> </p> <p> </p>
POA_UAV
<p>This dataset contains two set of images (trajectories) taken by a UAV equipped with a downward looking camera. The region in which the UAV flyed was at Porto Alegre (more specifically, Federal University of Rio Grande do Sul, Campus do Vale), a city from Rio Grande do Sul, Brazil. Besides the sets of images, there are also a folder with five satellite images from Campus do Vale and a README file. </p> <p> </p> <p>This dataset was used in the experiments of: </p> <p>M. Mantelli, D. Pittol, R. Neuland, A. Ribacki, R. Maffei, V. Jorge, E. Prestes, M. Kolberg, "A novel measurement model based on abBRIEF for global localization of a UAV over satellite images."</p>
A high-frequency and high-resolution image time series of the Gornergletscher - Swiss Alps - derived from repeated UAV surveys
<p>This dataset is based on aerial photographs of the Gornergletscher glacial system (Switzerland) collected during ten intensive UAV surveys carried out approximately every two weeks throughout the summer 2017.</p> <p>The final products consist in a series of 10 cm resolution ortho-images, Digital Elevation Models of the glacier surface, and Matching Maps that can be used to quantify ice surface displacements.</p>
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>
UAV RAW images from UAV at 50m altitude - Ria de Vigo (Spain) pilot site
<table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>UAV RAW images from UAV at 50m altitude - Ria de Vigo (Spain) pilot site</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>UAV RAW images from DJI Mini 2 UAV at 50m altitude in the area of Vao beach and Santa Marta - Limens beach at the Ria de Vigo pilot site. Data were aquired in March 2024 field campaign.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> UAV, Ria De Vigo, coastal area, raw drone images, DJI raw images</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Ria De Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p> UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> jpg</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p> Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p> None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p> Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> HSGME</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table>
UAV RAW images from UAV at 80m altitude - Ria de Vigo (Spain) pilot site
<table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>UAV RAW images from UAV at 80m altitude - Ria de Vigo (Spain) pilot site</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>UAV RAW images from DJI Mini 2 UAV at 80m altitude in the area of Vao beach and Santa Marta - Limens beach at the Ria de Vigo pilot site. Data were aquired in March 2024 field campaign.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> UAV, Ria De Vigo, coastal area, raw drone images, DJI raw images</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Ria De Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p> UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> jpg</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p> Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p> None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p> Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> HSGME</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> <td> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table>
UAV orthomosaic at 80m altitude - Ria de Vigo (Spain) pilot site
<table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>UAV orthomosaic at 80m altitude - Ria de Vigo (Spain) pilot site</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>UAV orthomosaic orthorectified with DGPS, compiled from images collected at 80m altitude using a DJI Mini 2 UAV, in the area of Vao beach and Santa Marta - Limens beach at the Ria de Vigo pilot site. Data were aquired in March 2024 field campaign.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> UAV, orthomosaic, Ria De Vigo, coastal area</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Ria De Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p> UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> 0.023m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> 0.25m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> EPSG:32630</p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p> Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p> None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p> Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> HSGME</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table>
UAV orthomosaic at 50m altitude - Ria de Vigo (Spain) pilot site
<table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>UAV orthomosaic at 50m altitude - Ria de Vigo (Spain) pilot site</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>UAV orthomosaic orthorectified with DGPS, compiled from images collected at 50m altitude using a DJI Mini 2 UAV, in the area of Vao beach and Santa Marta - Limens beach at the Ria de Vigo pilot site. Data were aquired in March 2024 field campaign.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p> UAV, orthomosaic, Ria De Vigo, coastal area</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p> Ria De Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p> English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p> UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p> 23.08.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p> Raster</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p> GeoTIFF</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p> 0.014m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p> 0.25m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p> EPSG:32630</p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p> Free</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p> None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p> Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p> HSGME</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p> <a href="mailto:izananiri@eagme.gr">izananiri@eagme.gr</a></p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p> English</p> </td> </tr> </tbody> </table>
Gummern - Mining Waste Deposits Multispectral UAV Imagery
<h2>Abstract</h2> <p>Mining Waste Deposits Multispectral UAV Imagery from DJI Mavic 3M</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Mining Waste Deposits Multispectral UAV Imagery</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Mining Waste Deposits Multispectral UAV Imagery from DJI Mavic 3M</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Multispectral, Mining Waste Deposits, UAV, Drone</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>UAV</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>06.09.2023</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>06.09.2023</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.05m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.25m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 4326</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UNILEON</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Enoc Sanz Ablanedo (esana@unileon.es)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.