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38 results for “unmanned aerial vehicles”
Unmanned aerial vehicles as a useful tool for investigating animal movements - samples
<p><span>Determining animal abundance is crucial for assessing the effectiveness of management measures against pest animals. Meanwhile, investigating animal movements has become important for conducting abundance estimations of unmarked animals since the random encounter model (REM)</span><span> was published. REM is a camera-trapping method </span><span>that derives animal density by using contact ratio between camera-traps and targeted animals that randomly move at a certain speed in a given area. However, it</span><span> requires an independent value, which is animal speed. F</span><span>or investigating animal speed, camera-traps with video recording and GPS tagging are the commonly used tools.</span></p> <p><span>U</span><span>nmanned aerial vehicles (UAVs) are currently used in wildlife monitoring to investigate the abundance of target species. It is evaluated as a tool that is non-invasive and suitable for surveys in inaccessible landscapes. Considering these characteristics, we regarded a distant survey using this technology as suitable for investigating animal movements. </span><span>Therefore, we proposed a method for estimating animal movements using UAVs and conducted a case study that aimed to investigate wild boars' movements.</span></p> <p><span>We collected 11 video samples that successfully followed the movements of wild boars from 26 UAV flights in total, and the average speed of their movements derived from all the samples was 1.54 km/24 h.</span></p> <p><span>We found that issues can be improved, including species identification, video sample length, and animal behaviours or activity patterns. On the other hand, our method showed potential for applying to species with specific characteristics in their body size, shape or activity patterns. With improvements in the issues mentioned above, UAVs would become an alternative tool for investigating animal movements.</span></p>
Unmanned Aerial Vehicle (UAV) image dataset.
<p>The dataset contains 2,919 images and separated into five classes of car, taxi, truck, bus and motorcycle.<br> </p>
Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research
Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.
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
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Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research
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Unmanned aerial vehicles as a useful tool for investigating animal movements - samples
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Snow depth mapping with an Unmanned Aerial Vehicles in heterogeneous mountain site (Izas Catchment, Pyrenees)
<p>Recent developments in unmanned aircraft vehicles (UAV) and Structure for Motion (SfM photogrammetry or simply SfM) algorithms have proved their worth for determining snow depth distribution. This dataset presents UAV and Terrestrial Laser Scanner (TLS) snow depth obsrevations obtained in complex alpine terrain in the Pyrenees. UAV observations have been acqired with a fixed-wing UAV working in RTK mode with an RGB camera. During the 2018-19 season, seven field campaigns (13 UAV flights) were undertaken covering 0.48 km2 . Several UAV observations were obtained under different light conditions and flight block configurations (altitude and image overlaps) in the same day with the aim if evaluating UAV observations when compared ti a well-established close range remote sensing technique (TLS).</p>
Field-based individual plant phenotyping of herbaceous species by unmanned aerial vehicle
<p>1. Recent advances in Unmanned Aerial Vehicle (UAVs) and image processing have made high-throughput field phenotyping possible at plot/canopy level in the mass grown experiment. Such techniques are now expected to be used for individual level phenotyping in the single grown experiment.</p> <p>2. We found two main challenges of phenotyping individual plants in the single grown experiment: plant segmentation from weedy backgrounds and the estimation of complex traits that are difficult to measure manurally.</p> <p>3. In this study, we proposed a methodological framework for field-based individual plant phenotyping by UAV. Two contributions, which are weed elimination for individual plant segmentation, and complex traits (volume and outline) extraction, have been developed. The framework demonstrated its utility in the phenotyping of<i> Helianthus tuberosus</i>(Jerusalem artichoke), an herbaceous perennial plant species.</p> <p>4. The proposed framework can be applied to either small and large scale phenotyping experiments.</p>
THE COMPLEX OF RECONNAISSANCE AND FIRE DESTRUCTION OF TARGETS BASED ON UNMANNED AERIAL VEHICLES
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A Two-time-level Model for Mission and Flight Planning of an Inhomogeneous Fleet of Unmanned Aerial Vehicles
<p>We consider the mission and flight planning problem for an inhomogeneous fleet of unmanned aerial vehicles (UAVs). Therein, the mission planning problem of assigning targets to a fleet of UAVs and the flight planning problem of finding optimal flight trajectories between a given set of waypoints are combined into one model and solved simultaneously. Thus, trajectories of an inhomogeneous fleet of UAVs have to be specified such that the sum of waypoint-related scores is maximized, considering technical and environmental constraints. Several aspects of an existing basic model are expanded to achieve a more detailed solution. A two-level time grid approach is presented to smooth the computed trajectories. The three-dimensional mission area can contain convex-shaped restricted airspaces and convex subareas where wind affects the flight trajectories. Furthermore, the flight dynamics are related to the mass change, due to fuel consumption, and the operating range of every UAV is altitude-dependent. A class of benchmark instances for collision avoidance is adapted and expanded to fit our model and we prove an upper bound on its objective value. Finally, the presented features and results are tested and discussed on several test instances using GUROBI as a state-of-the-art numerical solver.</p>
Glacial sediment-rich meltwater plume investigation using a high-resolution multispectral sensor embedded in an Unmanned Aerial Vehicle
<p>Methodology video</p>
Field‐based individual plant phenotyping of herbaceous species by unmanned aerial vehicle
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Replication Package: "When Uncertainty Leads to Unsafety: Empirical Insights into the Role of Uncertainty in Unmanned Aerial Vehicle Safety"
<p>Replication Package of the paper titled "When Uncertainty Leads to Unsafety: Empirical Insights into the Role of Uncertainty in Unmanned Aerial Vehicle Safety"</p>
A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles
<p>Datasets collected from the experiments described in the paper R. Milijas, L. Markovic, A. Ivanovic, F. Petric and S. Bogdan, "A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles," <em>2021 International Conference on Unmanned Aircraft Systems (ICUAS)</em>, 2021, pp. 1148-1154, doi: 10.1109/ICUAS51884.2021.9476802.</p> <p>The datasets consist of ROS bags which hold the UAV and LiDAR data, and of zip files which hold only the lidar data in binary format for non-ROS users.</p>
Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat
<p>It contains supplementary materials(revised version)and supporting data of Tables of Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat.<em> </em>However, the artical has not published. Data is available upon request.</p> <p>If you need anything, please don't hesitate to contact me(liujk@ahstu.edu.cn).</p>
Replication Package: "Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights"
<p><strong>Structure:</strong></p> <p>For each of the experiments, conducted for our Research questions, we include the following:</p> <p>1. An spreadsheet file containing the aggregated experiment results</p> <ul> <li>first tab: aggregated data for all the 10 repetitions, and metrics reported in the paper</li> <li>raw logs for each of 10 repetitions in separate tabs</li> </ul> <p>2. Experiment summary for each of the 10 repetitions</p> <ul> <li>plots of the evaluated solutions at each iteration, and their fitness values</li> <li>the plot of the overall progress of the fitness values over iterations</li> <li>the raw log containing the details of computations for each iteration</li> </ul> <p>3. Experiment simulation logs (raw simulation outputs)</p> <ul> <li>'.ulg' flight logs of the solutions evaluated at each iteration ( 'n' parallel simulations at each iteration)</li> <li>only the repetition with the best final result is included. We exclude the other repetitions since the total space needed is too large (more than 200 GB).</li> </ul>
Pictures of unmanned aerial vehicle (UAV) experiments on testing paper
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Aviation Safety Reporting System: Unmanned Aerial Vehicle (UAV) Reports
A sampling of reports involving Unmanned Aerial Vehicle (UAV) events.
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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.