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ShareScore release 0.9.0
Dataset results
7 results for “aircraft detection”
Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories
<p>Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"</p>
HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection
<p>The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.</p>
Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
<p>Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics' Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1400 kg/h. When emission rate estimates were generated using wind from High-Resolution Rapid Refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger's stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), accuracy of individual emission rate estimates produced using HRRR wind ranged from -64.1% to 87.0%. Across all controlled releases 38.1% of estimates had error within +/- 20%, and 87.3% of measurements were within a factor of two (-50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h) emission estimates are biased high; however, when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications which must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models, or to audit reported emission levels from oil and gas systems.</p>
Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
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Anomaly Detection dataset for the fuselage of an aircraft
<p>If you use the dataset, please cite:</p> <p><em>Siddhant Shete, Dennis Mronga</em></p> <p><strong>"Adaptive Online Anomaly Detection using Transfer Learning"</strong></p> <p>About the dataset: The dataset is basically used for anomaly detection in the fuselage of an aircraft manufacturing company. We captured the data on the mockup of the fuselage with several iterations at different distances away from the mockup. The dataset is basically the scans of mockup from top to bottom with and without anomalies. The dataset has been segregated into two panels.</p> <p>Contents of <em><strong> AircraftFuselageMockupDataset.zip </strong></em></p> <ol> <li>Nomal_panel1 </li> <li>Nomal_panel2</li> <li>Anomaly_panel1</li> <li>Anomaly_panel2</li> </ol> <p>Every folder has data at 3 distances 15cm, 25cm, 35cm.</p> <p> </p> <p><em>This dataset is provided by the Robotics Innivation Center, DFKI GmbH.</em></p> <p><em>The grant was provided by Federal Ministry for Economic Affairs and Climate Action </em></p> <p><em>Grant number: 20W1922F</em></p>
ADS-B anomaly detection in the surveillance of low-altitude aircrafts
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Data supporting: Multiple input tangential interpolation-driven damage detection of a jet trainer aircraft
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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.