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Benign effects of logging on aerial insectivorous bats in Southeast Asia revealed by remote sensing technologies
<b>Description: </b><p>Number of bat calls recorded by SongMeter bat 2 detectors set to record continuously on a trigger. Counts are classified into 21 acoustic call types, including 13 species.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/101"><b>Impacts of forest modification on bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK Natural Environment Research Council (NERC) (Human Modified Tropical Forests programme & a PhD scholarship jointly funded by University of Kent & NERC & EnvEast DTP scholarship, NE/L002582/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Economic Planning Unit of the Malaysian Government and the Sabah Biodiversity Council (Research licence UPE: 40/200/19/2723)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7740421">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_data_archive_Yoh2.xlsx</p><p><b>SAFE_data_archive_Yoh2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>All data </b> (described in worksheet MasterData)</p><p>Description: All auto and manual identifications for bat passes across a disturbance gradient, identified to functional group or species when possible</p><p>Number of fields: 13</p><p>Number of data rows: 134920</p><p>Fields: </p><ul><li><b>LOCATION</b>: Where the data was collected (Field type: location)</li><li><b>DATE</b>: Date surveyed (Field type: date)</li><li><b>TIME</b>: Time of recording (Field type: time)</li><li><b>AUTO_ID</b>: Taxa as identified using the automatic classifier (Field type: taxa)</li><li><b>ACCURACY</b>: Confidence value for auto identification results (Field type: numeric)</li><li><b>THRESLEVEL</b>: Whether the data met the desired auto-identification confidence value (Field type: categorical)</li><li><b>MANUAL_ID_CLEAN</b>: Taxa as identified manually (Field type: taxa)</li><li><b>FINAL_ID</b>: Final taxa label considering both the auto and manual ID (Field type: taxa)</li><li><b>TREATMENT</b>: Habitat type (Field type: categorical)</li><li><b>fc_100m</b>: Forest extent within 100m buffer of the survey location (Field type: numeric)</li><li><b>chm_100m</b>: Average canopy height within 100m buffer from survey location (Field type: numeric)</li><li><b>shape_100m</b>: Forest shape within 100m buffer of survey location (Field type: numeric)</li><li><b>TRI_100m</b>: Topographic ruggedness within 100m buffer of survey location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2011-04-01 to 2012-06-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  [CF_CROB] <br> -  -  -  -  -  [CF_H140] <br> -  -  -  -  -  [FMQCF1] <br> -  -  -  -  -  [FMQCF2] <br> -  -  -  -  -  [FMQCF3] <br> -  -  -  -  -  [FMQCF4] <br> -  -  -  -  -  [FMQCF5] <br> -  -  -  -  -  [FMQCF6] <br> -  -  -  -  -  [QCF] <br> -  -  -  -  -  [FM] <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus affinis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus creaghi</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus luctus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus philippinensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ater</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros galeritus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br></div><p></p>
Multi-Altitude Aerial Vehicles Dataset
<p><strong>Custom Multi-Altitude Aerial Vehicles Dataset:</strong></p> <p>Created for publishing results for ICUAS 2023 paper "<em>How High can you Detect? Improved accuracy and efficiency at varying altitudes for Aerial Vehicle Detection</em>", following the abstract of the paper.</p> <p><strong>Abstract</strong>—Object detection in aerial images is a challenging task mainly because of two factors, the objects of interest being really small, e.g. people or vehicles, making them indistinguishable from the background; and the features of objects being quite different at various altitudes. Especially, when utilizing Unmanned Aerial Vehicles (UAVs) to capture footage, the need for increased altitude to capture a larger field of view is quite high. In this paper, we investigate how to find the best solution for detecting vehicles in various altitudes, while utilizing a single CNN model. The conditions for choosing the best solution are the following; higher accuracy for most of the altitudes and real-time processing ( > 20 Frames per second (FPS) ) on an Nvidia Jetson Xavier NX embedded device. We collected footage of moving vehicles from altitudes of 50-500 meters with a 50-meter interval, including a roundabout and rooftop objects as noise for high altitude challenges. Then, a YoloV7 model was trained on each dataset of each altitude along with a dataset including all the images from all the altitudes. Finally, by conducting several training and evaluation experiments and image resizes we have chosen the best method of training objects on multiple altitudes to be the mixup dataset with all the altitudes, trained on a higher image size resolution, and then performing the detection using a smaller image resize to reduce the inference performance. The main results</p> <p>The creation of a custom dataset was necessary for altitude evaluation as no other datasets were available. To fulfill the requirements, the footage was captured using a small UAV hovering above a roundabout near the University of Cyprus campus, where several structures and buildings with solar panels and water tanks were visible at varying altitudes. The data were captured during a sunny day, ensuring bright and shadowless images. Images were extracted from the footage, and all data were annotated with a single class labeled as 'Car'. The dataset covered altitudes ranging from 50 to 500 meters with a 50-meter step, and all images were kept at their original high resolution of 3840x2160, presenting challenges for object detection. The data were split into 3 sets for training, validation, and testing, with the number of vehicles increasing as altitude increased, which was expected due to the larger field of view of the camera. Each folder consists of an aerial vehicle dataset captured at the corresponding altitude. For each altitude, the dataset annotations are generated in YOLO, COCO, and VOC formats. The dataset consists of the following images and detection objects:</p> <table> <tbody> <tr> <td><strong>Data</strong></td> <td><strong>Subset</strong></td> <td><strong>Images</strong></td> <td><strong>Cars</strong></td> </tr> <tr> <td>50m</td> <td>Train</td> <td>130</td> <td>269</td> </tr> <tr> <td>50m</td> <td>Test</td> <td>32</td> <td>66</td> </tr> <tr> <td>50m</td> <td>Valid</td> <td>33</td> <td>73</td> </tr> <tr> <td>100m</td> <td>Train</td> <td>246</td> <td>937</td> </tr> <tr> <td>100m</td> <td>Test</td> <td>61</td> <td>226</td> </tr> <tr> <td>100m</td> <td>Valid</td> <td>62</td> <td>250</td> </tr> <tr> <td>150m</td> <td>Train</td> <td>244</td> <td>1691</td> </tr> <tr> <td>150m</td> <td>Test</td> <td>61</td> <td>453</td> </tr> <tr> <td>150m</td> <td>Valid</td> <td>61</td> <td>426</td> </tr> <tr> <td>200m</td> <td>Train</td> <td>246</td> <td>1753</td> </tr> <tr> <td>200m</td> <td>Test</td> <td>61</td> <td>445</td> </tr> <tr> <td>200m</td> <td>Valid</td> <td>62</td> <td>424</td> </tr> <tr> <td>250m</td> <td>Train</td> <td>245</td> <td>3326</td> </tr> <tr> <td>250m</td> <td>Test</td> <td>61</td> <td>821</td> </tr> <tr> <td>250m</td> <td>Valid</td> <td>61</td> <td>823</td> </tr> <tr> <td>300m</td> <td>Train</td> <td>246</td> <td>6250</td> </tr> <tr> <td>300m</td> <td>Test</td> <td>61</td> <td>1553</td> </tr> <tr> <td>300m</td> <td>Valid</td> <td>62</td> <td>1585</td> </tr> <tr> <td>350m</td> <td>Train</td> <td>246</td> <td>10741</td> </tr> <tr> <td>350m</td> <td>Test</td> <td>61</td> <td>2591</td> </tr> <tr> <td>350m</td> <td>Valid</td> <td>62</td> <td>2687</td> </tr> <tr> <td>400m</td> <td>Train</td> <td>245</td> <td>20072</td> </tr> <tr> <td>400m</td> <td>Test</td> <td>61</td> <td>4974</td> </tr> <tr> <td>400m</td> <td>Valid</td> <td>61</td> <td>4924</td> </tr> <tr> <td>450m</td> <td>Train</td> <td>246</td> <td>31794</td> </tr> <tr> <td>450m</td> <td>Test</td> <td>61</td> <td>7887</td> </tr> <tr> <td>450m</td> <td>Valid</td> <td>61</td> <td>7880</td> </tr> <tr> <td>500m</td> <td>Train</td> <td>270</td> <td>49782</td> </tr> <tr> <td>500m</td> <td>Test</td> <td>67</td> <td>12426</td> </tr> <tr> <td>500m</td> <td>Valid</td> <td>68</td> <td>12541</td> </tr> <tr> <td>mix_alt</td> <td>Train</td> <td>2364</td> <td>126615</td> </tr> <tr> <td>mix_alt</td> <td>Test</td> <td>587</td> <td>31442</td> </tr> <tr> <td>mix_alt</td> <td>Valid</td> <td>593</td> <td>31613</td> </tr> </tbody> </table> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Phylogenetic data for: Synchrospora gen. nov., a new Peronosporaceae genus with aerial lifestyle from a natural cloud forest in Panama
<p class="MsoNormal"><span>During a survey of <em>Phytophthora</em> diversity in Panama, fast-growing oomycete isolates were obtained from naturally fallen leaves of an unidentified tree species in a tropical cloud forest. Phylogenetic analyses of sequences from the nuclear ITS, LSU and ß–tubulin loci and the mitochondrial <em>cox1</em> and <em>cox2</em> genes revealed they belong to a new species of a new genus, officially described here as <em>Synchrospora</em> gen. nov., which resided as a basal genus within the Peronosporaceae. The type species <em>S. medusiformis</em> has unique morphological characters. The sporangiophores show determinate growth, multifurcating at the end forming a stunted, candelabra-like apex from which multiple (8 to >100) long, curved pedicels are growing simultaneously in a medusa-like way. The caducous papillate sporangia mature and are shed synchronously. The breeding system is homothallic, hence more inbreeding than outcrossing, with smooth-walled oogonia, plerotic oospores and paragynous antheridia. Optimum and maximum temperatures for growth are 22.5 and 25–27.5 °C, consistent with its natural cloud forest habitat. It is concluded that <em>S. medusiformis</em> is adapted to a lifestyle as canopy-dwelling leaf pathogen in tropical cloud forests. More oomycete surveys in the canopies of tropical rainforests and cloud forests are needed to elucidate the diversity and role of oomycetes and, in particular, <em>S. medusiformis</em> and possibly other <em>Synchrospora</em> taxa in this as yet under-explored habitat.</span></p>
Automated processing of aerial imagery for geohazards monitoring: Results from Fagradalsfjall eruption, SW Iceland, August 2022
<p><strong>1-</strong> <strong>Dataset Summary</strong><br> Here we present a dataset of DEMs (Digital Elevation Models), orthomosaics, and lava area outlines for the August 2022 eruption at Fagradalsfjall, SW Iceland. The dataset consists of: (1) five aerial surveys collected over the course of the August 2022 Fagradalsfjall eruption, (2) one survey carried out on 14 August 2022 using Pléiades satellite stereo images, and (3) a larger aerial survey, covering the 2021 and 2022 eruption sites in late September 2022 after the volcanic activity concluded.</p> <p><strong>2- Background</strong></p> <p>The volcano at Fagradalsfjall, SW-Iceland, began erupting on 3 August 2022 at 13:20 following 10 months of quiescence. As part of the response plan, a series of photogrammetric surveys were conducted in rapid, operational mode throughout the duration of the eruption. Subsequent production of data products for natural hazards monitoring (lava maps, lava volumes, effusion rates) were calculated within hours and reported to the Icelandic Civil Defense, following a similar approach that described in Pedersen et al., 2022a and in Gouhier et al., 2022. At the start of the 2022 eruption, GCPs had not yet been placed around the new fissure, but reference data (orthomosaics and DEMs) which had been georeferenced using targets measured with differential GNSS existed of the eruption site from September 2021 from Pedersen et al. (2022b) were available to use as a reference in the new workflow instead of GCPs. Due to the urgent need from authorities for information about the new eruption, a processing method that avoids the time-consuming task of manual GCP selection using a reference image for georeferencing was preferable in this instance. Besides the acquisition of aerial photographs, the CIEST2 initiative was also re-activated to collect Pléiades stereo images in emergency mode (Gouhier et al., 2022).</p> <p><strong>3 – Overview of data collection</strong></p> <p>Table 1 contains the overview of the surveys collected and presented in this repository.</p> <p> Table 1. Summary of surveys included in this dataset, by survey date.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Date & Time</strong><br> <strong>YYYYMMDD HH:MM</strong></p> </td> <td> <p><strong>Sensor</strong></p> </td> <td> <p><strong>Platform</strong></p> </td> <td> <p><strong>Flight alt.</strong><br> <strong>(m asl)</strong></p> </td> <td> <p><strong>Images</strong></p> </td> <td> <p><strong>Surveyed</strong><br> <strong>km<sup>2</sup></strong></p> </td> </tr> <tr> <td> <p>20220803 17:05</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203*</p> </td> <td> <p>~ 850</p> </td> <td> <p>46</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>20220804 11:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 2100</p> </td> <td> <p>32</p> </td> <td> <p>35</p> </td> </tr> <tr> <td> <p>20220813 09:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>~ 750</p> </td> <td> <p>123</p> </td> <td> <p>9</p> </td> </tr> <tr> <td> <p>20220814 13:00</p> </td> <td> <p>Pléiades</p> </td> <td> <p>PHR1B</p> </td> <td> <p>n/a</p> </td> <td> <p>2</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>20220815 08:15</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>20</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>20220816 10:06</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-203</p> </td> <td> <p>2100</p> </td> <td> <p>19</p> </td> <td> <p>26</p> </td> </tr> <tr> <td> <p>20220926 12:00</p> </td> <td> <p>A6D</p> </td> <td> <p>TF-BMW**</p> </td> <td> <p>2100</p> </td> <td> <p>~20</p> </td> <td> <p>18</p> </td> </tr> </tbody> </table> <p>* TF-203: Savannah S aircraft</p> <p>** TF-BMW: Vulcanair P68 Observer 2 aircraft, operated by Garðaflug ehf.</p> <p><strong>4- Methods</strong></p> <p><strong>4.1 Processing of the aerial photographs from 3-16 Aug 2022</strong><br> Throughout the eruption, aerial surveys were conducted using a Hasselblad A6D 100 MP camera with 35 mm focal lens, from a height of 750 – 2,100 m above ground over the active lava field from an ultralight aircraft with a window in the bottom to allow for vertical photos to be taken (see supplement of Pedersen et al., 2022a for details and images of the setup). The camera was manually triggered to give ~70% overlap, and approximate flight lines were prepared beforehand for use with a handheld GPS during the flight to give ~30 % side overlap.<br> <br> An automated processing pipeline was created in python, which leverages tools from the Ames Stereo Pipeline (ASP, Shean et al., 2016) and Agisoft Metashape stand-alone Python API (v. 1.8.4). The processing and georeferencing of the aerial data were done in three steps, with all steps being automated except for the digitization of lava outlines. First, using a very high-resolution reference orthomosaic and DEM created in September 2021 and georeferenced with ground control points (Pedersen et al., 2022b), interest points (IPs) in each image were matched with the reference dataset, using the ASP routine <em>ipfind. </em>This created GCPs for each image over stable terrain. Second, hillshades were created from both the reference DEM and the source dataset DEM and matches in IPs were found in both, creating a second round of ground control points to refine the georeferencing of the entire block. Finally, the alignment of the source DEM was refined using the <em>dem_align</em> (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM.</p> <p><strong>4.2 Processing of the Pléiades stereo images</strong><br> The Pléiades stereo images were processed using the Ames Stereo Pipeline, using the general workflow of <em>mapproject </em>and <em>parallel_stereo </em>(e.g., Deschamps-Berger et al., 2020). The <em>parallel_stereo </em>routine used default arguments, plus the following arguments:</p> <p><em>--stereo-algorithm asp_mgm -t rpcmaprpc --corr-seed-mode 3 --corr-max-levels 2 --cost-mode 3 --subpixel-mode 9 --corr-kernel 7 7 --subpixel-kernel 15 15</em></p> <p>We used the DEM from 4 Aug 2022 as the reference for <em>mapproject </em>and for the final DEM co-registration applied to the produced Pléiades DEM.</p> <p><strong>4.3 Processing of the 26 September 2022 dataset</strong><br> The survey from 26 September 2022 was collected and processed using direct georeferencing from an on-board GPS antenna. The final alignment of the block was refined using the dem_align (demcoreg) protocol from Shean et al. (2016) by applying a bulk linear shift in X, Y and Z which minimizes the vertical difference in stable terrain between the source and reference DEM. Because this survey covered a much larger area, the reference DEM for the final coregistration was the <a href="https://atlas.lmi.is/mapview/?application=DEM">ÍslandsDEM </a>v.1.0 (Landmælingar Íslands, 2022).</p> <p><strong>4.4. Maps of the lava outlines, lava thickness, lava volume, Time Average Effusion Rate (TADR)</strong><br> For each survey, a differential DEM (dDEM) showing elevation changes since the 2021 eruption was created by subtracting the reference DEM (ÍslandsDEM v.1.0, which includes the post-eruption DEM from Pedersen et al., 2022a) from the source DEM. Lava outlines, lava thickness lava volume, TADR and uncertainties were calculated using the methods described in Pedersen et al., 2022a. Table 2 summarizes calculations from this dataset.</p> <p> </p> <p>Table 2. Summary of survey results calculated from August 2022 Fagradalsfjall eruption DEMs and orthomosaics.</p> <table align="center"> <thead> <tr> <th> <p><strong> Date Start</strong></p> </th> <th> <p><strong>Date End</strong></p> </th> <th> <p><strong>Time </strong></p> <p><strong>Difference</strong></p> </th> <th> <p><strong>Lava </strong></p> <p><strong>Area<sup>*</sup> End </strong></p> <p><strong>(km<sup>2</sup>)</strong></p> </th> <th> <p><strong>dh<sup>**</sup> </strong></p> <p><strong>(m)</strong></p> </th> <th> <p><strong>Volume<sup>+</sup></strong></p> <p><strong>End<br> (1e+6 m<sup>3</sup>)</strong></p> </th> <th> <p><strong>TADR<sup>++</sup><br> (m<sup>3</sup>/s)</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>20220803<br> 13:20</p> </td> <td> <p>20220803<br> 17:05</p> </td> <td> <p>0d 03h 45m</p> </td> <td> <p>0.07</p> </td> <td> <p>5.88</p> </td> <td> <p>0.43</p> <p>± 0.03</p> </td> <td> <p>32.1</p> <p>± 1.5</p> </td> </tr> <tr> <td> <p>20220803<br> 17:05</p> </td> <td> <p>20220804<br> 11:00</p> </td> <td> <p>0d 21h 40m</p> </td> <td> <p>0.14</p> </td> <td> <p>11.13 </p> </td> <td> <p>1.57</p> <p>± 0.05</p> </td> <td> <p>17.7</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220804<br> 11:00</p> </td> <td> <p>20220813<br> 09:00</p> </td> <td> <p>8d 22h 00m</p> </td> <td> <p>1.27<sup>#</sup></p> </td> <td> <p>6.90</p> </td> <td> <p>10.33</p> <p>± 0.6</p> </td> <td> <p>11.4</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220813<br> 13:08</p> </td> <td> <p>20220814<br> 13:00</p> </td> <td> <p>0d 23h 52m</p> </td> <td> <p>1.24</p> </td> <td> <p>7.28</p> </td> <td> <p>10.62</p> <p>± 0.70</p> </td> <td> <p>2.8</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220814<br> 13:00</p> </td> <td> <p>20220815<br> 08:15</p> </td> <td> <p>0d 19h 15m</p> </td> <td> <p>1.26</p> </td> <td> <p>7.46</p> </td> <td> <p>10.99</p> <p>± 0.55</p> </td> <td> <p>4.1</p> <p>± 0.8</p> </td> </tr> <tr> <td> <p>20220815<br> 08:15</p> </td> <td> <p>20220816<br> 10:16</p> </td> <td> <p>1d 2h 01m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.49</p> </td> <td> <p>11.13</p> <p>± 0.53</p> </td> <td> <p>2.0</p> <p>± 0.7</p> </td> </tr> <tr> <td> <p>20220816<br> 10:16</p> </td> <td> <p>20220821<br> 06:00<sup>##</sup></p> </td> <td> <p>4d 19h 44m</p> </td> <td> <p>1.28</p> </td> <td> <p>7.69</p> </td> <td> <p>11.39</p> <p>± 0.44</p> </td> <td> <p>0.653</p> <p>± 0.10</p> </td> </tr> </tbody> </table> <p><sup>*</sup>Total area of the lava field since 2022-08-03 before activity started.</p> <p><sup>**</sup>dh end is the mean thickness of the lava flow-field in the end of the given period.</p> <p><sup>+</sup>Volume erupted since 2022-08-03 before activity started.</p> <p><sup>++</sup>Time-averaged discharge rate for the given period</p> <p><sup>#</sup>Extrapolated value. Survey does not cover entire active lava area.</p> <p><sup>##</sup>End Time: 21 August 2022, 6:00. This time deduced from field observations from members of the Institute of Earth Sciences, University of Iceland. Values calculated from 26 September 2022 dataset.</p> <p>Figures and visual summaries of the processing method, resulting lava volumes, and uncertainties can be found in this poster: <a href="https://ftp.lmi.is/stm/Sydney/Fagradalsfjall_Aug2022/poster_faf_automated_proc2_srg_2022.pdf">Fagradalsfjall August 2022</a>.<br> <br> Orthomosaics from this dataset are viewable online at: <a href="https://atlas.lmi.is/mapview/?application=umbrotasja">https://atlas.lmi.is/mapview/?application=umbrotasja</a></p> <p><strong>Data naming conventions:</strong></p> <ul> <li>Data type: DEM, ortho, outline, diffDEM, lavafree, lava</li> <li>Acquisition date: YYYYMMDD_HHMM</li> <li>Platform/Sensor for data collection: Pléiades (PLE), Hasselblad A6D from aircraft (A6D)</li> <li>Resolution: 2x2 m (DEMs) and 30x30 cm (Orthomosaics)</li> <li>Folders (by survey): YYYYMMDD_HHMM_platform (during eruption) or 'posteruption'_platform (sensors/platform: A6D or PLE)</li> </ul> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https:/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Raster compression system: ZSTD (<a href="http://facebook.github.io/zstd/">http://facebook.github.io/zstd/</a>)</li> <li>Vector data format: GeoPackage (<a href="https://www.geopackage.org/">https://www.geopackage.org/</a>)</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul>
Increasing Bridge Durability and Service Life with LIDAR Enhanced Unmanned Aerial Systems (UAS)
<p>Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In the future, if it can be automatized, it can be implemented to simplify the complexity of inspections. The authors developed a platform to mount the camera, sonar laser, and DAQ on the UAS and remotely controlled the data collection operation. Additionally, an algorithm was developed which can automatically obtain the geometric information of the rebar. The proposed automated RGBD-equipped UAS system was developed, fabricated, and tested in the Balloon Fiesta Park on a simulated bridge deck at different heights and with different UAS motions to obtain the best distance, speed, and motion for real construction field. The authors also conducted an outdoor experiment in a construction field at White Rock to validate the capability of the proposed system on the real site with vertical rebar and the challenges of the real construction site. The result confirmed that the LIDAR-equipped UAS system has the potential to help the inspection process in terms of time, accuracy, safety, and generating a permanent record of the inspection. Bridge construction information collected by LiDAR-equipped UAS technology can eventually provide bridge managers with transparent condition assessment and one-step decision-making support through quantitative measurement combined with 3D visualization to facilitate repair planning that can greatly facilitate maintenance.</p>
Quantifying the age-structure of free-ranging delphinid populations: testing the accuracy of Unoccupied Aerial System-photogrammetry
<p><span>Understanding the population health status of long-lived and slow-reproducing species is critical for their management. However, it can take decades with traditional monitoring techniques to detect population-level changes in demographic parameters. Early detection of the effects of environmental and anthropogenic stressors on vital rates would aid in forecasting changes in population dynamics and therefore inform management efforts. Changes in vital rates strongly correlate with deviations in population growth, highlighting the need for novel approaches that can provide early warning signs of population decline (e.g., changes in age-structure). We tested a novel and frequentist approach, using Unoccupied Aerial System- (UAS) photogrammetry, to assess the population age-structure of small delphinids. First, we measured the precision and accuracy of UAS-photogrammetry in estimating total body length (TL) of trained bottlenose dolphins (<em>Tursiops</em> <em>truncatus</em>). Using a log-transformed linear model, we estimated TL using the blowhole-to-dorsal-fin-distance (BHDF) for surfacing animals. To test the performance of UAS-photogrammetry to age-classify individuals, we then used length measurements from a 35-year dataset from a free-ranging bottlenose dolphin community to simulate UAS-estimates of BHDF and TL. We tested five age-classifiers and determined where young individuals (<10 years) were assigned when misclassified. Finally, we tested whether UAS-simulated BHDF only or the associated TL estimates provided better classifications. TL of surfacing dolphins was overestimated by 3.3% ±3.1% based on UAS-estimated BHDF. Our age-classifiers performed best in predicting age-class when using broader and fewer (two and three) age-class bins with ~80% and ~72% assignment performance, respectively. Overall, 72.5-93% of the individuals were correctly classified within two years of their actual age-class bin. Similar classification performances were obtained using both proxies. UAS-photogrammetry is a non-invasive, inexpensive, and effective method to estimate TL and age-class of free-swimming dolphins. UAS-photogrammetry can facilitate the detection of early signs of population changes, which can provide important insights for timely management decisions.</span></p>
1968 Aerial Photos of Dasht-e Bayaz Earthquake, Iran
<p>These are both georeferenced and original scans of 1:7,500 scale black and white aerial photographs collected in 1968 following the M7.1 Dasht-e Bayaz earthquake in Northeast Iran. They were originally presented by Ambraseys and Tchalenko (1969), however their original collection source is unattributed. <br> <br> Ambraseys, N.N., Tchalenko, J.S., 1969. The Dasht-E Bayaz (Iran) Earthquake of August 31, 1968: A Field Report. Bulletin of the Seismological Society of America 59, 1751–1792.</p>
High resolution LiDAR dataset acquired using UAV (unmanned aerial vehicle) over two vineyards and two years located in 'Tomiño', Pontevedra, Spain.
<p>This dataset features an extensive collection of LiDAR data from vineyards in northern Spain, targeting vineyards to address the growing demand for public UAV LiDAR datasets in Agricultural Sciences. The data was gathered using a DJI M300 multi-rotor platform equipped with a DJI Zenmuse L1 LiDAR sensor, conducting UAV flights at 20, 30, and 50 meters Above Ground Level (AGL) across two vineyards during 2021 and 2022. The dataset comprises ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. This information is essential for studying vineyard morphology and development and plays a key role in refining vineyard management tactics. In addition, the dataset is valuable for agricultural robotics, providing detailed terrain and canopy data crucial for designing efficient flight paths and navigation algorithms. Finally, it serves as a true "ground truth" dataset to verify satellite-derived models, enabling the generation of high-precision digital elevation models (DEMs) and other derivatives.</p>
Beaver aerial surveys in Ohio
<p>Dataset of aerial surveys for beaver presence and abundance on 54 40x40 km plots in Ohio between 2013 and 2020 and variables used to model relative abundance (with number of beaver lodges as a proxy)</p>
The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions - dataset
<p>This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:</p> <ol> <li>Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where: <ul> <li>[location] is the place of data collection (e.g., Cocora, Vinicucna)</li> <li>[year] is the year of data collection (e.g., 2023)</li> <li>[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade</li> <li>[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)</li> </ul> </li> <li> <p>Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).</p> </li> </ol> <p>Structure-from-motio<span> </span>n processing was performed in Agisoft Metashape (<a href="https://www.agisoft.com/">https://www.agisoft.com/</a>, Agisoft, 2023). Mapping was performed in ArcGIS Pro (<a href="https://www.esri.com/en-us/arcgis/about-arcgis/overview">https://www.esri.com/en-us/arcgis/about-arcgis/overview</a>, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).</p> <p>Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. <em>International Journal of Applied Earth Observations and Geoinformation</em>, 103474. doi:<a href="https://doi.org/10.1016/j.jag.2023.103474"> https://doi.org/10.1016/j.jag.2023.103474</a></p>
Changes in Health and Functional Status in Patients With Chronic Obstructive Pulmonary Disease During Therapy With Spiolto® Respimat® (AERIAL®)
ClinicalTrials.gov study NCT03165045. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Flight data from: Acrobatics at the insect-scale: A durable, precise, and agile micro-aerial-robot
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Data from: Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
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Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
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Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing
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Data from: Aerial survey of sea ducks and whales in winter in eastern Canadian Arctic
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A global analysis of aerial displays in passerines revealed an effect of habitat, mating system and migratory traits
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Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales
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The positive influence of wetlands on reproductive success and body mass in an aerial insectivore is more pronounced in intensively cropped agroecosystems
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Crop performance, aerial, and satellite data from multistate maize yield trials
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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.