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533 results for “Aerial”
Spatial modelling of aerial survey data reveals an important European storm-petrel hotspot and its underlying drivers within the North-East Atlantic
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Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift
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Using unoccupied aerial vehicles to estimate availability and group size error for aerial surveys of coastal dolphins
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Large marine predator aerial survey data for Hauraki Gulf, New Zealand
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Georectified Mosaic of Aerial Images of Baltimore City in 1927
Landscape analyses are typically done using spatially explicit color aerial imagery. However, working with non-spatial black and white historical aerial photographs presents several challenges that require a combination of techniques and approaches. We analyzed 93 aerial images covering 544 km2 (210 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. The images were taken from a biplane between October 1926 and February 1927. High-resolution scans were georeferenced and georectified against modern satellite imagery of the area and then combined to create a single raster mosaic. This process converted the images from a disparate set of photographs into a spatially explicit GIS data set that can be used to observe changes in land patches over time—and ultimately integrated with other long-term social, economic, and ecological data.
A curated dataset of aerial survey images over the central Congo Basin, 1958
<p>This dataset contains a subset data from the Belgian Science Policy Office funded “Congo basin eco-climatological data recovery and valorisation" project (COBECORE, contract BR/175/A3/COBECORE).</p> <p>The data included is curated and pre-processed aerial survey imagery as used in a a land-use land-cover change analysis "Historical aerial surveys map long-term changes of forest cover and structure in the central Congo Basin".</p> <p> The dataset includes:</p> <ul> <li>the pre-processed images (aerial_images.tar.gz)</li> <li>the meta-data associated with the aerial images (flight_paths*)</li> <li>the final orthomosaic (yangambi_orthomosaic.tif)</li> </ul> <p>For the full methodology we refer to the full paper:</p> <p><strong>Hufkens K.</strong>, et al. (2020) Historical Aerial Surveys Map Long-Term Changes of Forest Cover and Structure in the Central Congo Basin. <strong> Remote Sensing</strong>, 12, 638.</p> <p>Please cite the work as such.</p> <p> </p>
Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
<p>This dataset comprises of the IDL code referenced in the 'Open Research' section of the Kaye and Pittman (2020) study 'Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system' published in <em>Methods in Ecology and Evolution</em> (<a href="https://doi.org/10.1111/2041-210X.13402">https://doi.org/10.1111/2041-210X.13402</a>).</p> <p>This study describes a proof‐of‐concept autonomous unmanned aerial vehicle (UAV) system that utilizes the fluorescence characteristics unique to different materials to scan and acquire targets in the field e.g. fossils, rocks and minerals, organisms and archaeological artefacts. This is possible because these targets are often highly fluorescent against lower fluorescence backgrounds and may exhibit different colours. Fluorescence is stimulated by a near‐UV laser that is projected across the ground as a horizontal line directly below the UAV. The IDL code is for laser line and colour extractions in the laser scan strip. The raw .jpeg data for the IDL code is not provided here as this depends on what target is being scanned. All image data are made available in the paper. Additional contextual information is provided in the '2 MATERIALS AND METHODS' section of the paper, especially in Figure 3.</p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales
1.Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine-scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field-based habitat quality measurements and lower spatial resolution, higher spatial extent satellite-based remote sensing. Our goal in this study was to evaluate the potential for UAS structure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush-steppe environment. 2.At the plant and patch scales, we compared UAS-derived estimates of vegetation height, volume (estimate of food availability), and canopy cover to estimates from ground-based terrestrial laser scanning (TLS), and field-based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho, USA, using point clouds derived from UAS SfM. 3.At the individual plant scale, the UAS-derived estimates matched those from TLS for height (r2 = 0.85), volume (r2 = 0.94), and canopy cover (r2 = 0.68). However, there was less agreement with field-based measurements of height (r2 = 0.67), volume (r2 = 0.31), and canopy cover (r2 = 0.29). At the patch scale, UAS-derived estimates provided a better fit to field-based measurements (r2 = 0.51-0.78) than at the plant scale. Landscape-scale maps created from UAS were able to distinguish structural heterogeneity between key patch types. 4.Our work demonstrates that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine-resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation, and land management.
A laser-microfabricated electrohydrodynamic thruster forcentimeter-scale aerial robots
<p>To date, insect scale robots capable of controlled flight have used flapping wings for generating lift, but this requires a complex and failure-prone mechanism. A simpler alternative is electrohydrodynamic (EHD) thrust, which requires no moving mechanical parts. In EHD, corona discharge generates a flow of ions in an electric field between two electrodes; the high-velocity ions transfer their kinetic energy to neutral air molecules through collisions, accelerating the gas and creating thrust. We introduce a fabrication process for EHD thruster based on 355 nm laser micromachining and our approach allows for greater flexibility in materials selection. Our four-thruster device measures 1.8 * 2.5 cm and is composed of steel emitters and a lightweight carbon fiber mesh. The current and thrust characteristics of each individual thruster of the quad thruster is determined and agrees with Townsend relation. The mass of the quad thruster is 37 mg and the measured thrust is greater than its weight (362.6 µN). The robot is able to lift off at a voltage of 4.6 kV with a thrust to weight ratio of 1.38.</p>
Aerial survey - low res
Low resolution version of an aerial survey using a drone, data tied to OSGB36 using GPS Source: Objaverse 1.0 / Sketchfab
Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset
<p><strong>Dataset description</strong></p><p>This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.</p><p>It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.</p><p><strong>Folder configuration</strong></p><p>The dataset contains 4 folders for different lighting conditions:</p><ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul><p>Each contains UAV and UGV folders. UAV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul><p>UGV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul><p><strong>Data format</strong></p><p>The dataset includes</p><ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul><p><strong>File naming convention</strong></p><p>Each data is named "s_i{_segmented, _annotation}.ext", where:</p><ul><li><strong>s</strong> denotes the simulated time in seconds.</li><li><strong>i</strong> denotes the index counting every 10ms of simulated time.</li><li><strong>ext</strong> denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels <strong>_segmented</strong> and <strong>_annotation </strong>appended to the name for segmentation image and object annotations, respectively.</li></ul><p>Each segmented image uses the following RGB color mapping:</p><ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>
WW2 Radar Aerial Base
Four concrete blocks that formed the base of a wooden aerial in the remains of the World War 2 Chain Home Radar Station that are located about 1km south east of Shaugh Prior on Dartmoor, Devon, England. https://www.heritagegateway.org.uk/Gateway/Results_Single.aspx?uid=1123713&resourceID=19191 646 photos taken in August 2021 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Caerhun Roman Fort Aerial Parch Marks
Caerhun (Roman Canovium) was founded somewhere around 77-78 AD during Agricola's campaigns in North Wales. It lies at a strategic crossing on the west bank of the River Conwy and also controlled the coastal road between the legionary fortresses at Chester (Deva) and Caernarfon (Segontium). The auxiliary unit which occupied the fort is not known, but a tile of the 20th Legion is reported to have been found in 1696. The fort was remodelled in the 2nd century AD with a stone-walled face replacing the earlier clay and rubble rampart. The fort was potentially abandoned for a while after 150 AD, but finds of 3rd and 4th century AD date suggest occupation at this time. The church of St. Mary's in the north east corner of the fort overlies former barrack blocks and is believed to be early Medieval in origin, but the fabric visible today dates to between the 13th and 19th centuries. The dry weather has caused the grass to die back over buried wall features which now appear as light brown markings in the grass Source: Objaverse 1.0 / Sketchfab
Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Lublin 1944 aerial images with spatial overlay index
<p><em><strong>1. Lublin 1944 aerial image overlay index</strong></em> [.geojson or .gml file] is a vectorized, digital form of selected overlay indexes for degree square 51N022E (https://catalog.archives.gov/id/44241929) of German Flown Aerial Photographs,1939-1945 (https://catalog.archives.gov/id/306065) archived in National Archives and Records Administration, College Park, MD.</p> <p>2. The vectorized index contains geometries, attributes and other metadata of 104 aerial images of Lublin [Poland] captured by Luftwaffe reconaissance from 10th May 1944 to 6th December 1944.</p> <p>3. The dataset contains the archive of 104 digital copies of aerial images, scaned with A2-3050-Sharp363N. The images are .jpg files with 24-bit colour depth and resolution 600 dpi. File size: from 7,5 MB to 20 MB.</p> <p>4. The mosaic of aerial images is uploaded in Ortofotomapa_1944_modificado_3.tif - 0,8GB file. TFW, AUX and OVR files added for GIS users. TPK file with ESRI tiled package is added. This is also available via spatial data services (TMS and WMTS):</p> <p>- XYZ/TMS layers available at <strong><em>//ortolub.umcs.pl/data/tiles_3857/{z}/{x}/{y}.png </em></strong>or via https://ortolub.umcs.pl/map_en.html</p> <p>- WMTS layer available at <strong><em>//tiles.arcgis.com/tiles/STaxETJ8DGWEoQ8D/arcgis/rest/services/Ortolub_1944/MapServer </em></strong>or via ArcGIS Online https://www.arcgis.com/home/item.html?id=d5dd97b49b014d6ea0e6bc221fc37668</p> <p>5. The mosaic cropped to 1931-1947 city boundaries are added: Lublin_1944_aerial_10k_600dpi_gsc.jpg and Lublin_1944_aerial_adm_10k_600dpi_gsc.jpg with area outside the boundaries masked. This is also available at Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Lublin_1944_aerial_image.jpg</p> <p>6. The project and the platform <em><strong>https://ortolub.umcs.pl</strong></em> was developed under the Polish National Science Centre grant programme - Miniatura 4.0. ref. no. 2020/04/X/HS4/00382. I hereby share my work under Creative Commons license CC BY-SA 4.0 (Attribution - ShareAlike).</p>
Doodleverse/Segmentation Zoo Res-UNet models for identifying water in oblique aerial photos of coasts.
<p><strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in oblique aerial photos of coasts.</strong></p> <p>These model data are based on images of coasts and associated labels. Models have been fitted to the following types of data</p> <p>1. RGB (3 band): red, green, blue</p> <p>Classes are: {0: water, 1: land}.</p> <p>These files are used in conjunction with Segmentation Zoo*</p> <p>For each model, there are 3 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p> </p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images</p> <p> </p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p> </p> <p>References</p> <p>* https://github.com/Doodleverse/segmentation_zoo</p> <p>** https://github.com/Doodleverse/segmentation_gym</p>
Population structure, patterns of natal dispersal, and demographic history in a declining aerial insectivore, the purple martin Progne subis
<p>Genetic variation is a fundamental component of biodiversity, and studying population structure, gene flow, and demographic history can help guide conservation strategies for many species. Like other aerial insectivores, the purple martin (<em>Progne subis</em>) is in decline, and yet their genetic background remains largely unknown. To address this knowledge gap, we assessed population structure in the nominate eastern subspecies (<em>P. s. subis</em>) with relation to natal dispersal and examined historical genetic patterns in all three subspecies (<em>P. s. subis, P. s. arboricola, P. s. hesperia</em>) across their North American breeding range by estimating effective population sizes over time. We used next-generation sequencing strategies for genomic analyses, integrating whole-genome resequencing data with continent-wide band encounter records to examine natal dispersal. We documented population structure across <em>P. s. subis</em>, with the highest differentiation between the northern (Alberta) and more southern colonies and following patterns of isolation-by-distance. Consistent with spatial patterns of genetic differentiation, we also found greater longitudinal than latitudinal natal dispersal distances, signifying potential latitudinal constraints on gene flow. Earlier contractions in effective population sizes in the western <em>P. s. arboricola</em> and <em>P. s. hesperia</em> compared to the eastern <em>P. s. subis</em> subspecies suggest these subspecies originated from two different glacial refugia. Together, these findings support latitudinal distinction in <em>P. s. subis</em>, and elucidate the origin of subspecies differentiation, highlighting the importance to conserve populations across the range to maximize genetic diversity and adaptive potential in the purple martin.</p>
A global analysis of aerial displays in passerines revealed an effect of habitat, mating system and migratory traits
<p><span>Aerial displaying is a flamboyant part of the sexual behaviour of several volant animal groups, including birds. Nevertheless, little attention has been focused on identifying correlates of large-scale diversity in this trait. In this study, we scored the presence and absence of aerial displays in males of 1,732 species of passerine birds (Passeriformes) and employed Bayesian phylogenetically informed mixed models to test for associations between aerial displays and a set of life-history and environmental predictors. Our multivariate models revealed that species with males that perform aerial displays inhabited open rather than closed (forested) habitats. These species also exhibited higher levels of polygyny, had more elongated wings, migrated over longer distances and bred at higher latitudes. When we included species where the sexual function of displays has not been explicitly described but is likely to occur, we found that aerial displaying was also associated with smaller body size and increased male plumage colouration. Our results suggest that both sexual selection and natural selection have been important sources of selection on aerial displays in passerines.</span></p>
ScienceDex guides
Understand access before you commit
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.