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Figure 23 in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 23. Methods of changing the angle of attack of the inner wing. A, left lateral view of Anhanguera configured as in Fig. 18. The broken line indicates the chord line. The angle of attack of the section is zero. B, as in (A), but with the left leg depressed at the hip by 30°. The geometric angle of attack a is indicated. C, as in (A), but with the leg supinated at the hip by 30°. The leg movements shown in (B) and (C) increase both the angle of attack and the camber of the inner wing.

Figure 24 in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 24. Effects of pteroid depression. A, left lateral view of Anhanguera as in Fig. 23. B, as in (A), but with the left pteroid depressed by 30°. The camber is increased and the angle of attack, a, is reduced by this movement.

Figure 21. A in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 21. A, typical plots of pitching moment M against angle of attack a (measured with respect to the zero-lift angle of attack) for stable and unstable aircraft. Both aircraft are shown balancing at the same equilibrium angle of attack, at which point the pitching moment is zero. B, the effect of raising the elevators (1) on the longitudinal balance of a stable aircraft: the pitching moment is increased, as is the equilibrium angle of attack, so the aircraft pitches up (2) until equilibrium is restored. C, the effect of raising the elevators (1) on the longitudinal balance of an unstable aircraft: the pitching moment is increased, but the equilibrium angle of attack is reduced, so the aircraft now pitches up away from equilibrium (2).

Figure 15. A in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 15. A, reconstructed articular surfaces of the right carpometacarpal joint of Coloborhynchus robustus. Elements are oriented as in Fig. 7: distal syncarpal in lateral view and wing metacarpal in medial view. Scale bar: 50 mm. B, diagrammatic representation of (A), showing contact areas in the close-packed position and the joint axis. For a list of anatomical/arthrological abbreviations, see Appendix 1.

Figure 22 in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 22. Increasing the equilibrium angle of attack of an unstable aircraft can be brought about by decreasing the derivative dM/da, where M is the pitching moment and a is the angle of attack, by sweeping the wings back (A), or by decreasing the zero-lift pitching moment M0, by depressing the pteroids for example (B). These adjustments can theoretically be used in response to an unstable nose-up pitch (1), thus establishing a new equilibrium (2).

Figure 19. A in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 19. A, Stable but unbalanced wing profile, with the centre of gravity (c.g.) situated ahead of the mean aerodynamic centre (m.a.c.). B, stable and balanced configuration, with a small tailplane set at a negative incidence with respect to the main wing.

opencc-by-4.0Sep 2021View details →

At-sensor-radiance data from aerial imaging for Lake Mulargia (Sardinia, Italy) (2020/09/24)

This dataset contains the radiance data collected from Hyspex images of Lake Mulargia (Sardinia, Italy) for the VNIR bands. The acquisition was done by CGR Spa (Italy).

opencc-by-4.0Sep 2021View details →

Dataset for Agile 5G Network Measurements: Operator Benefits of Employing Aerial Mobility

Dataset of paper "Agile 5G Network Measurements: Operator Benefits of Employing Aerial Mobility". The measurement data contains two different scenarios: Scenario 1 and Scenario 2, according to the paper. These two datasets include two different kinds of files: those related to measurements taken with a phone and those taken with a drone (UAV).

opencc-zeroNov 2022View details →

Using unoccupied aerial vehicles to estimate availability and group size error for aerial surveys of coastal dolphins

Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals for visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs or drones) to collect video of dolphins to characterize availability and investigate errors surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with the Australian humpback dolphin Sousa sahulensis off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (se = 0.26) and 39.27 sec (se = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7-sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions, and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.

opencc-by-4.0Jul 2022View details →

A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

Summary Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, residential PV installations are deployed at an unprecedented pace, and their safe integration into the grid necessitates up-to-date, high-quality information. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, segmentation masks, and installation metadata. We provide installation metadata for more than 28000 installations. We provide ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets. This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", published in Scientific data. The article is accessible here : <a href="https://www.nature.com/articles/s41597-023-01951-4">https://www.nature.com/articles/s41597-023-01951-4</a> These complete records consist of: <ol> <li>The complete training dataset containing RGB overhead imagery, segmentation masks and metadata of PV installations (folder bdappv),</li> <li>The raw crowdsourcing data, and the postprocessed data for replication and validation (folder data).</li> </ol> Data records Folders are organized as follows: <ul> <li>bdappv/ Root data folder <ul> <li>google / ign:  One folder for each campaign <ul> <li>img/: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN.</li> <li>mask/: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN.</li> </ul> </li> <li>metadata.csv The <code>.csv</code>  file with the installations' metadata.</li> </ul> </li> </ul>   <ul> <li>data/ Root data folder <ul> <li>raw/ Folder containing the raw crowdsourcing data and raw metadata; <ul> <li>input-google.json: <code>.json </code>input data data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the first annotation campaign;</li> <li>input-ign.json: <code>.json </code>input data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the second annotation campaign;</li> <li>raw-metadata.json: <code>.json </code>output containing the PV systems’ metadata extracted from the BDPV database before filtering. It can be used to replicate the association between the installations and the segmentation masks, as done in the notebook metadata.</li> </ul> </li> <li>replication/ Folder containing the compiled data used to generate the segmentation masks; <ul> <li>campaign-google/campaign-ign: One folder for each campaign <ul> <li>click-analysis.json: <code>.json </code>output on the click analysis, compiling raw input into a few best-guess locations for the PV arrays. This dataset enables the replication of our annotations,</li> <li>polygon-analysis.json: <code>.json </code>output of polygon analysis, compiling raw input into a best-guess polygon for the PV arrays.</li> </ul> </li> </ul> </li> <li>validation/ Folder containing the compiled data used for technical validation. <ul> <li>campaign-google/campaign-ign: One folder for each campaign <ul> <li>click-analysis-thres=1.0.json: <code>.json </code>output of the click analysis with a lowered threshold to analyze the effect of the threshold on image classification, as done in the notebook annotation;</li> <li>polygon-analysis-thres=1.0.json: <code>.json </code>output of polygon analysis, with a lowered threshold to analyze the effect of the threshold on polygon annotation, as done in the notebook annotations.</li> </ul> </li> <li>metadata.csv: the <code>.csv </code>file of filtered installations' metadata.</li> </ul> </li> </ul> </li> </ul> License We extracted the thumbnails contained in the google/img/ folder using Google Earth Engine API and we generated the thumbnails contained in the ign/img/ folder from high resolution tiles downloaded from the online IGN portal accessible here: <a href="https://geoservices.ign.fr/bdortho">https://geoservices.ign.fr/bdortho</a>. Images provided by Google are subjet to Google's terms and conditions. Images provided by the IGN are subject to an open license 2.0. Access the terms and conditions of Google images at this URL: <a href="https://www.google.com/intl/en/help/legalnotices_maps/">https://www.google.com/intl/en/help/legalnotices_maps/</a> Access the terms and conditions of IGN images at this URL: <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf</a>

opencc-by-4.0Jan 2023View details →

Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty

CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.

opencc-zeroFeb 2023View details →

HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

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opencc-zeroMar 2023View details →

Counting animals in aerial images with a density map estimation model

Animal abundance estimation is increasingly based on drone or aerial survey photography. Manual post-processing has been used extensively, however, volumes of such data are increasing, necessitating some level of automation, either for complete counting or as a labour-saving tool. Any automated processing can be challenging when using such tools on species that nest in close formation such as Pygoscelis penguins. We present here a customized CNN-based density map estimation method for counting of penguins from low-resolution aerial photography. Our model, an indirect regression algorithm, performed significantly better in terms of counting accuracy than standard detection algorithm (Faster RCNN) when counting small objects from low-resolution images and gave an error rate of only 0.8 percent. Density map estimation methods as demonstrated here can vastly improve our ability to count animals in tight aggregations, and demonstrably improve monitoring efforts from aerial imagery.

opencc-by-4.0Apr 2023View details →

SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation

The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA. The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively. The structure of the data is as follows: 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks. 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels. Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type. 1-Panels-Ortho     |----Train     |----Test     -----Validation 1-Panels-Mask     |----Train     |----Test     -----Validation 2-NoPanels-Ortho     |----Train     |----Test     -----Validation

opencc-zeroApr 2023View details →

Large marine predator aerial survey data for Hauraki Gulf, New Zealand

Large marine predators, such as cetaceans and sharks, play a crucial role in maintaining biodiversity patterns and ecosystem health. Despite the recognised importance of these animals and their over-representation as threatened species, distribution data at appropriate temporal and spatial scales is often lacking or insufficient for effective conservation. Here, we present sightings of large marine megafauna recorded from a replicate systematic aerial survey undertaken in the Hauraki Gulf, Aotearoa New Zealand during a full year. Using flexible machine learning models (Boosted Regression Tree models), we use these sightings data to investigate relationships between large marine predator occurrence (Bryde's whales, common and bottlenose dolphins, bronze whalers, pelagic and immature hammerhead sharks) and spatially explicit environmental and biotic variables to predict species richness of large marine predators and investigate their fine-scale spatiotemporal distribution patterns. All models were considered informative (all, AUC > 0.78), and temporally dynamic variables, such as the distribution of prey, were important in predicting the occurrence of the study species and species groups. Our approach and data highlight the value of multi-species surveys and the importance of considering temporally variable abiotic and biotic drivers for understanding biodiversity patterns when informing ecosystem-scale conservation planning and dynamic ocean management. We provide data files of: <ul> <li>Locations of species presence / pseudo absence location over time (in .csv format) and associated environmental and biotic variables for Bryde's whales, common and bottlenose dolphins, bronze whaler, pelagic and immature hammerhead sharks.</li> <li>Monthly estimates of 14 high-resolution spatially explicit environmental and biotic variables (1 km grid resolution): Bathymetry; Slope; Distance from shore; Distance to 40m depth; Sand; Mud; Gravel; Seabed disturbance; Tidal current; Chlorophyll a; Sea surface temp; Distance to plankton; Distance to prey (saved as .R data)</li> <li>Model objects, R code, and model outputs of the Boosted Regression Tree modelling.</li> <li>Predicted monthly distributions (and associated spatially explicit uncertainty) of Bryde's whales, common dolphins, bottlenose dolphins, bronze whaler sharks, pelagic sharks, immature hammerhead sharks, and richness of large marine predators in the Hauraki Gulf, New Zealand, (January to December). Monthly richness estimates of large marine predators.</li> </ul>

opencc-by-4.0Aug 2023View details →

Historical Aerial Photografies of the Rio Grande do Sul

This data paper discusses a collection of aerial photographs, which are scientific images obtained through cameras fixed on balloons, planes, drones, and other manned or crewless aerial vehicles. Initially, they were used to conducting studies on exploring space and landscape and territorial analysis. The images are a physical collection from a university in southern Brazil. The objective is to justify aerial photographs as research data in Geosciences.The study methodology is based on aerial photographs collected between 1960 and 2000. For 40 years, they were taken in road projects by the Autonomous Department of Highways (DAER RS), which had a Sector of Topography and Aerophotogrammetry. With its plane, the sector carried out around 240 aerial surveys between 1969 and 2001 (flight scales between 1: 2,000 to 1: 20,000), where around 60,000 photographs were generated and printed based on 170 rolls of film. The sample of pictures from this data paper is part of the raw data of the thesis entitled "Morphodynamic complexity and anthropogenic diachronic in the northern coastal plain of Rio Grande do Sul." (Rocha, 2023), which is in progress. The work consists of identifying and characterizing geological indicators of environmental changes since the 1940s in a stretch of the north coast of Rio Grande do Sul, Brazil. RMK-ZeISS was then scanned with an A3 scanner with a resolution of 1200 Dpi. The methods and conditions used to collect data from aerial photographs are the same as listed; flight conditions, camera quality, flight height, and the scale of the photo are directly related to the generation of aerial photography. When an aerial photograph is printed analog, it does not have information on geographic coordinates, geometric attributes, time, and spatial or topological relationships; they are not georeferenced. Thus, even after digitization, the image needs to be cataloged and transformed into a georeferenced image format, the so-called geodata, such as GeoTiff, for later availability in geographic databases and used by geographic information systems. The plane's camera parameters are essential information for this type of survey data. For example, what was the plane? What is the flight speed and height? Visibility, flight date, time, and flight time? What is the region, flight ranges, and specific purpose of aerial mapping? What type of camera, lens focus, type of film used, film thickness, and filters? Finally, what is the developer equipment, and the development date?

publicApr 2024View details →

Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest

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publicApr 2022View details →

Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds

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publicFeb 2022View details →

Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping

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