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155 results for “Vehicle Data”
Metadata and antimicrobial resistance gene count data from dusts collected on Canadian vehicle filters
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Data for: Direct and indirect ecosystem responses to vehicle compaction of soft sediments
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Data from: Data collection of Odometer images via WhatsApp to measure vehicle miles traveled report
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Estimated roadway segment traffic data by vehicle class for the United States: A machine learning approach
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Data from: Vehicle-mounted cameras reveal negative impact of the Fukushima Daiichi nuclear power plant accident on large-bodied bird abundance via paddy field abandonment
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Data for: Research and application of bag filter system for railway ballast bed coal suction vehicles
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Data from: Engineered nucleocytosolic vehicles for loading of programmable editors
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Data from: US-Mexico second-hand electric vehicle trade: Battery circularity and end-of-life policy implications
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Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips
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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 few large roads or many small ones? How to accommodate growth in vehicle numbers to minimise impacts on wildlife
Roads and vehicular traffic are among the most pervasive of threats to biodiversity because they fragmenting habitat, increasing mortality and opening up new areas for the exploitation of natural resources. However, the number of vehicles on roads is increasing rapidly and this is likely to continue into the future, putting increased pressure on wildlife populations. Consequently, a major challenge is the planning of road networks to accommodate increased numbers of vehicles, while minimising impacts on wildlife. Nonetheless, we currently have few principles for guiding decisions on road network planning to reduce impacts on wildlife in real landscapes. We addressed this issue by developing an approach for quantifying the impact on wildlife mortality of two alternative mechanisms for accommodating growth in vehicle numbers: (1) increasing the number of roads, and (2) increasing traffic volumes on existing roads. We applied this approach to a koala (Phascolarctos cinereus) population in eastern Australia and quantified the relative impact of each strategy on mortality. We show that, in most cases, accommodating growth in traffic through increases in volumes on existing roads has a lower impact than building new roads. An exception is where the existing road network has very low road density, but very high traffic volumes on each road. These findings have important implications for how we design road networks to reduce their impacts on biodiversity.
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 from: Interspecific analysis of vehicle avoidance behavior in birds
Among the most widespread forms of anthropogenic modification of the natural landscape is road construction, with vehicle mortality a major issue affecting amphibians, reptiles, mammals, and birds. Why some species are more susceptible to vehicle collision than others, however, is poorly understood. We examine how roadside vegetation patterns, road size, vehicle speed, and brain size influence vehicle avoidance behavior using more than 3700 individuals of 11 species of European birds. We find that on larger roads and at higher vehicle speeds, birds were more likely to fly away from the road than to cross it. Moreover, species with a larger relative brain size flew away from the road more often than species with a small brain size, something that may in part explain interspecies differences in vehicle collision mortality rates. Our results provide important insights into factors that influence vehicle avoidance behavior in birds and show that brain size can be an important trait for adjusting to novelties in their environment.
Simulated SAR data of vehicles on a background
<p>The simulated data set is used as input to a convolutional neural network to train the classifier.</p>
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>
Master Thesis- Modeling of Electric Vehicle Charging Infrastructure and Comparison of Electric Vehicle Load Simulation with Empirical Charging Data
<p>All the data behind relevant plots in the thesis report are stored here</p>
Quantitative evidence for modelling electric vehicles - Supplementary Data
<p><strong>Please cite as:</strong></p> <p>Malte Jansen, Rob Gross, and Iain Staffell. ‘Quantitative Evidence for Modelling Electric Vehicles’. <em>Renewable and Sustainable Energy Reviews</em> 199 (1 July 2024): 114524. <a href="https://doi.org/10.1016/j.rser.2024.114524">https://doi.org/10.1016/j.rser.2024.114524</a>.</p> <p><strong>Abstract:</strong></p> <p>Electric vehicles are now a major contributor to decarbonising the transport sector. Their rollout has accelerated rapidly since 2020, reaching a global fleet of 40 million in 2023. This presents both problems and opportunities for electricity systems, with charging increasing peak loads, but also providing a large new source of flexibility to help manage increased shares of wind and solar generation, shift peak demand and improve network management.</p> <p>While EV flexibility is widely discussed, there is uncertainty surrounding the magnitude to which EVs could help electricity systems, and a distinct lack of quantitative evidence around adoption, charging behaviour and technical capabilities for load shifting. This study employs the rapid evidence assessment method to synthesise recent information. We find that studies expect that EVs could provide 1–11 GW of flexible capacity per million vehicles (median: 3.7 GW), with the ability to shift demand by 1.5–5 hours (median: 4 hours) and a price elasticity of –0.77 to –0.10 (median: –0.15). Diurnal profiles of charging demand and availability for providing flexibility are aggregated across multiple studies. The results are relevant for energy modellers and show that the interaction between EVs and electricity systems can be generalised on a widely-applicable basis.</p>
Data from: Experimental evidence supports the ability of spotted lanternfly to hitchhike on vehicle exteriors as a mechanism for anthropogenic dispersal
<p>This data is a companion to this paper: </p> <p>Johanna E Elsensohn, Scott Wolford, Amy Tabb, Tracy Leskey, “Experimental evidence supports the ability of spotted lanternfly to hitchhike on vehicle exteriors as a mechanism for anthropogenic dispersal,” 2024, Royal Society Open Science 11:240493. <a href="https://doi.org/10.1098/rsos.240493">doi:10.1098/rsos.240493</a>. </p> <p><br><strong>Manually-measured data</strong></p> <p>Experiments are detailed in the paper and data is contained in the table in this data release. Details of the data are available in the paper; we also summarize and define acronyms contained in the table here.</p> <p>Stage: insect life cycle stage. Values are 1<sup>st</sup>, 2<sup>nd</sup>, 3<sup>rd</sup>, 4<sup>th</sup> instars, early adult, and late adult.</p> <p>Location: location on the vehicle where the insect was placed for the experiment. Values, and their USA equivalents:</p> <p>- bonnet = hood.</p> <p>- nose wing = side panel.</p> <p>- scuttle panel = cowl panel.</p> <p>- wiper blade = wiper.</p> <p>- windscreen = windshield.</p> <p>Acclim.: means that the insect was allowed an acclimation period. 1 = yes there was an acclimation period, 0 = no there was not an acclimation period.</p> <p>Max RPM reached (0/1) : the insect remained attached to the vehicle at the maximum revolutions per minute (RPM) of the blower fan, 1850 RPM, equivalent to wind speed output was 100 ± 5 km/h 60cm from the housing exhaust. </p> <p>Max RPM reached: the maximum revolutions per minute (RPM) of the blower fan at which time the insect was detached from the vehicle.</p> <p>Windspeed (ft/min): conversion of insect detachment RPM (column 7) to feet/minute.</p> <p>Windspeed (KPH): conversion of insect detachment RPM (column 7) to windspeed in kilometers/hour.</p> <p>Body size: values are null (.), small (s), and (large). The body size is only assessed for the adult life stages; all instar stages have null. The adult is considered ‘small’ if the lateral yellow area on the insect’s underside was concave or flat and less than 2 mm wide. The insect was labelled ‘large’ if the lateral yellow area was ≥ 2 mm wide and convex.</p> <p>Sex (m/f): sex (male, female) was determined for the adult stages only. All of the instar stages have the value null (.).</p>
Dataset for "Learning Scene Semantics from Vehicle-centric Data for City-scale Digital Twins", Fürntratt et al.
<p>Dataset for "Learning Scene Semantics from Vehicle-centric Data for City-scale Digital Twins", Fürntratt et al.</p> <p>Data are anonymized and provided with segmentation mask ground truths. </p>
Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters, Data
<p>Data for Paper "Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters"</p>
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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.