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533 results for “Aerial”
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Figure 1 in Quantitative assessment of species identification in aerial transect surveys for ice-associated seals
Figure 1. The characteristic bands on the coats of ribbon seals are not necessarily clearly visible in an aerial image. The images on the top right and bottom right were taken with a Canon 1Ds Mark III fitted with a Zeiss 100 mm lens from 300 m during a 2012 line transect survey in the Bering Sea. In the top right image, an observer would likely rely on the clearly visible bands to conclude that the seal is certainly a ribbon seal. In the bottom right image, an observer would likely rely on a combination of body shape, head size, flipper size and shape, and what could be one or more bands to conclude that the seal is probably a ribbon seal.
Figure 3 in Quantitative assessment of species identification in aerial transect surveys for ice-associated seals
Figure 3. Observed species and age class identification probabilities for four species of iceassociated seals in the Bering Sea. True species and age classes include spotted seal pup (SDP), spotted seal nonpup (SDN), ribbon seal pup (RNP), ribbon seal nonpup (RNN), bearded seal pup (BDP), bearded seal nonpup (BDN), ringed seal pup (RDP), and ringed seal nonpup (RDN). Observed species classifications include spotted seal (red), ribbon seal (green), bearded seal (yellow), ringed seal (blue), and unknown seal (white). Observed age classes include pup, nonpup, and unknown. Solid colors with no hashing indicate unknown age classification. Top panel (a) includes results from an analysis with no observer effects on model parameters. Bottom four panels (b) correspond to four different observers from an analysis including observer effects.
Figure 2. A in Quantitative assessment of species identification in aerial transect surveys for ice-associated seals
Figure 2. A red face, which is one of the characteristics associated mostly with bearded seals, is not always present, nor is it necessarily visible in an aerial image. The image on the right was taken with a Canon 1Ds Mark III fitted with a Zeiss 100 mm lens from 300 m during a 2012 line transect survey in the Bering Sea. In this image, an observer would likely rely on the combination of body shape, head size, front-flipper size and shape, and position on the floe to conclude that the seal is probably or certainly a bearded seal.
Fig. 3 in Response of male Centruroides vittatus (Scorpiones: Buthidae) to aerial and substrate-borne chemical signals
Fig. 3: The movement of a male scorpion during a 3-minute trial. Scorpion position was marked by a dot every 5 seconds of the trial. In general, the movement path has more space between dots in the "no-stimulus" side (top) versus the "stimulus" side (bottom). The average distance (+/- standard deviation) traveled between 5 second plots: no-stimulus side = 15.3 +/- 7.8 mm (median = 12.5 mm), stimulus side = 10.8 +/- 5.9 mm (median = 8.0 mm). "BL" indicates occurrence of a backward lunge.
Fig. 1 in Response of male Centruroides vittatus (Scorpiones: Buthidae) to aerial and substrate-borne chemical signals
Fig. 1: Y-shaped arena for Experiments 1, 2, and 3 to test airborne chemical communication in C. vittatus.
Figure 2 in Aerial insects avoid fluorescing scorpions
Figure 2: Mean difference (with 95% confidence intervals) between aerial insects captured on sticky traps bearing fluorescent scorpions and sticky traps bearing non-fluorescing scorpions, using data pooled from new moon nights (total n=45) and full moon nights (total n=47).
Figure 1 in Aerial insects avoid fluorescing scorpions
Figure 1: Mean difference (with 95% confidence intervals) between aerial insects captured on sticky traps bearing fluorescing scorpions and sticky traps bearing non-fluorescing scorpions on six nights during the summer of 2004. Values> 0 indicate that more aerial insects were captured on traps bearing fluorescing scorpions than non-fluorescing scorpions, while values <0 indicate the reverse.
Dataset used in "Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle". https://doi.org/10.5194/hess-2017-625.
<p>Dataset used in</p> <p>Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle</p> <p>Filippo Bandini<sup>1</sup>, Daniel Olesen<sup>2</sup>, Jakob Jakobsen<sup>2</sup>, Cecile Marie Margaretha Kittel<sup>1</sup>, Sheng Wang<sup>1</sup>, Monica Garcia<sup>1</sup>, and Peter Bauer-Gottwein<sup>1</sup></p> <ul> <li><sup>1</sup>Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark</li> <li><sup>2</sup>National Space Institute, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark</li> </ul> <p><strong>Hydrol. Earth Syst. Sci.</strong></p> <p><strong>https://doi.org/10.5194/hess-2017-625</strong></p> <p> </p> <p>The dataset contains</p> <p>-data/observations that were used to obtain the figures shown in the paper. Data have .mat extension (Binary data container format used by MATLAB; may include arrays, variables, functions, and other types of data;)</p> <p>-scripts to compute statistics and plot data, with .m extension (contain MATLAB code, either in the form of a script or a function)</p> <p>-shape files (shp — shape format; the feature geometry itself, .shx — shape index format, .dbf — attribute format, .prj — projection format; .sbn and .sbx — spatial index of the features, .cpg — used to specify the code page, .<em>qpj</em> QGIS projection file) or raster files (.geotiff) to reproduce the map contents reported in the referenced paper.</p> <p>The repository is subdivided into directories containing the dataset shown in the paper. These directories are named with the figures and/or tables numbers of the referenced paper. </p>
Aerial Telemanipulation System
<p>Aerial Telemanipulation System</p> <p>1. Introduction</p> <p>Dataset name: aerial_telemanipulation_system_dataset.zip<br> This dataset contains the experimental data of the aerial telemanipulation system (with force feedback and bilateral control) developed in WP4 for the AEROARMS project.<br> The dataset is part of the deliverables D9.4 (First data management plan).</p> <p>2. Standards and metadata</p> <p>The acquired data are available in the following formats:<br> 1. standard .txt format, to be used in any standard software.<br> 2. standard .mat format, to be used in the matlab software.</p> <p>A readme file with the description of acquired data been added to the dataset folder in order to facilitate sharing and re-usability.</p> <p> </p>
A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants
<p>This folder contain the data relative to the contact based pipe inspection presented on M. Tognon et al. "A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants." IEEE Robotics and Automation Letters 4.2 (2019): 1846-1851.</p>
Cooperative aerial tele-manipulation with haptic feedback
<p>Data-set regarding the work "Cooperative aerial tele-manipulation with haptic feedback".</p> <p>Citation bibtex:</p> <p>@inproceedings{mohammadi2016cooperative,<br> title={Cooperative aerial tele-manipulation with haptic feedback},<br> author={Mohammadi, Mostafa and Franchi, Antonio and Barcelli, Davide and Prattichizzo, Domenico},<br> booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},<br> pages={5092--5098},<br> year={2016},<br> organization={IEEE}<br> }<br> </p>
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments [dataset]
<p>This dataset is related to "Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments" in IEEE RA-L,2019.</p> <p> </p> <p>Data Capture<br> ========================<br> Data is obtained by manually flying the UAV through the redwood forest environment using a FrSky Taranis (Plus) Digital Telemetry Radio System. In total, 81,674 frames were captured together with the flight behaviour that comprehends flights under and above the forest canopy, navigation inside caves and on river beds, lakes and mountains.</p> <p> </p> <p>Folder Structure<br> ========================<br> |-manual_0 - manual_5: sequences containing training data</p> <p>|-test_0 - sequences containing testing data</p> <p> </p> <p>Data Protection<br> ========================<br> Gathered by simulated flight using Microsoft AirSim (2019) and released in accordance with MSR Aerial Information and Robotics Simulator (AirSim) lisence, which is described in details bellow:</p> <p> </p> <blockquote> <p>The MIT License (MIT)</p> <p>MSR Aerial Informatics and Robotics Platform<br> MSR Aerial Informatics and Robotics Simulator (AirSim)<br> Copyright (c) Microsoft Corporation<br> All rights reserved.<br> MIT License</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the ""Software""), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:<br> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.<br> THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p> </blockquote>
Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)
<p>We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower. </p>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Figure 4 in A comparison of image and observer based aerial surveys of narwhal
Figure 4. Detection function plot of chosen DS model when sightings of both aerial observer pairs were pooled. Intercept obtained from the MR model. The solid black line indicates the probability detection function from the DS model and the open symbols indicate the probability of each detection given its perpendicular distance and other covariate values.
Figure 3. Detection functions for Observer pair 1 in A comparison of image and observer based aerial surveys of narwhal
Figure 3. Detection functions for Observer pair 1 (upper panel) and Observer pair 2 (lower panel) during aerial surveys in Melville Bay from 25 to 30 August 2014. Data are truncated at 1,300 m.
Figure 2 in A comparison of image and observer based aerial surveys of narwhal
Figure 2. Narwhal sighting locations in the Melville Bay survey area in 2014 recorded by aerial observers in real-time and identified by analysts in digital imagery. Most of "observer only" sightings are detections beyond the area covered by images (i.e., beyond 500 m from the trackline). The image of ice distribution was of 30 August 2014. We acknowledge the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov/) operated by the NASA/Goddard Space Flight Center Earth Science Data and Information System (ESDIS) project.
Figure 1 in A comparison of image and observer based aerial surveys of narwhal
Figure 1. Survey strata and transects designed for the aerial survey in Melville Bay from 25 to 30 August 2014. Transects that were surveyed zero, two, or three times, are marked in blue, green or red, respectively. All black transects were surveyed once.
Fig. 1 in Potential of unmanned aerial sampling for monitoring insect populations in rice fields
Fig. 1. Rotary-wing unmanned aerial vehicle equipped with remote-controlled insect net openings (a). Layout of double-layered insect net designed to prevent loss of insect samples during aerial sampling (b). Representative flight path of unmanned aerial vehicle for aerial sampling over rice field (c).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.