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81 results for “sonar”
Sidescan Sonar Substrate, Depth, and Shadow Image-Label-Pairs
<p>Substrate, depth, and shadow image-label pairs used to train side scan sonar segmentation models v1.0 implemented in PINGMapper v2.0.</p><p> </p><p>Images were labeled with <a href="https://github.com/Doodleverse/dash_doodler">Doodler</a> and <a href="https://www.makesense.ai/">Make Sense.</a></p>
Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments
<p><strong>Context</strong></p><p>This dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads,<strong> </strong>DOI: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977024">10.1109/OCEANS47191.2022.9977024</a></p><p>This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions.</p><p>This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image.</p><p> </p><p><strong>Content</strong></p><p>This repository contains three .zip folders each containing data collected in a different lab or field trial.</p><ul><li>'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany.</li><li>'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany.</li></ul><p>Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file.</p><p>For more details and meta-information on the collected data please refer to "data_description.json" included in this repository.</p><p>Additional tools for handling and preparing the data can be found under <a href="https://github.com/DeeperSense/oceans_2022">https://github.com/DeeperSense/oceans_2022</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</p><p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).</p>
Underwater surveys of mullet schools (Mugil liza) with Adaptive Resolution Imaging Sonar
<p>This dataset is part of a research project that employs deep learning, with a density-based regression approach, to count fish in low-resolution sonar images (Tarling et al. preprint arXiv DOI: http://arxiv.org/abs/2104.14964).</p> <p>In this repository, we provide data from sonar-based underwater videos of schools of migratory mullets (<em>Mugil liza</em>) recorded at the Tesoura beach (28.495775 S, 48.759996 W), a 100-meter long beach at the inlet canal connecting the Laguna lagoon system to the Atlantic Ocean, in southern Brazil. Since the water transparency at the lagoon canal is very low (from 0.3 to 1.5m visibility; collected <em>in situ</em> with a Secchi disk), mullet schools were recorded by deploying an Adaptive Resolution Imaging Sonar, ARIS 3000 (Sound Metrics Corp, WA, USA), which uses 128 beams to project a wedge-shaped volume of acoustic energy and convert their returning echoes into a digital overhead view of the mullet schools.</p> <p>This dataset contains 500 fully annotated images that were manually marked for the location and abundance of mullet fish, and 126 raw sonar video files, representing over 100k images. The files are organized as follows:</p> <p>1) "2018-MM-DD_HHMMSS" files are mp4 videos (you may need to add the file extension ".mp4"): There are 126 ARIS files converted into MP4 videos totalling over 789MB of underwater footage captured at 3 frames/seconds. Note that file names indicate the date and time the video was recorded.</p> <p>2) ".npy" files (in Labelled_data.zip): From the video files, 500 images were selected for labelling. Images (x) were cropped to represent a 4x8.5m<sup>2</sup> area and resized to 320 x 576 pixels. Mullet fish were marked with a point annotation. Corresponding ground truth density maps (y) were generated by convolving a Gaussian kernel over the image mask, size =4 and standard deviation = 1. The labelled dataset was randomly split into a holdout partition of 350 training images, 70 validation, and 80 test. </p> <p>3) ".csv" files: log of frames selected for the labelled subset of data</p> <p>4) ".h5" file: pre-trained weights for our multi-task with uncertainty regularisation network</p> <p>To advance the development of these machine learning tools, we also make our code openly available (https://github.com/ptarling/DeepLearningFishCounting).</p>
North Temperate Lakes LTER: Pelagic Prey - Sonar Data 2001 - current
Total pelagic fish abundance data were collected annually in mid-summer (following stratification) using hydroacoustic surveys along a set of transects in each of eight lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, Mendota, Monona, and Fish), from 1981-1999 (Lakes Monona and Fish from 1995). Hydroacoustic data were not collected in 2000, but collection resumed in 2001 on Crystal, Sparkling, and Trout and continues to the present (with the exception of Crystal in 2004). In 2005, Lake Mendota was included in the annual sampling rotation. This dataset includes data from lakes Crystal, Sparkling, Trout and Mendota from 2001 through present. Lake Mendota was not surveyed in 2020 due to COVID-19. Big Muskellunge Lake was included back into the sampling rotation in 2020 and continues to be surveyed annually. Sonar data represent 1) species-specific whole lake density estimates and average length (target strength (dB)); 2) depth-stratified (1m) species-specific density estimates and average length by 200m transects; and 3) individual depth-specific target length (not assigned to species). Dataset names for the aforementioned are below. 1) North Temperate Lakes LTER: Pelagic Prey Whole Lake Density - Sonar Data 2) North Temperate Lakes LTER: Pelagic Prey Interval Density - Sonar Data 3) North Temperate Lakes LTER: Pelagic Prey Single Target - Sonar Data Although rare, large targets representing predatory species were excluded from the density estimation for pelagic prey species (when n<3 were caught in vertical gillnets) using the proportion of large targets identified during single target analysis on each lake. Pelagic fish densities for lakes Sparkling, Crystal, Big Muskellunge, and Mendota are for the entire basin of each lake. The data shown for Trout Lake represent densities in the south basin only. Number of current sites: 5
Data and Computer scripts for 'Place recognition using batlike sonar' (eLife)
<p>The paper ‘Place recognition using batlike sonar’ can be freely accessed online at http://dx.doi.org/10.7554/eLife.14188. Contents, including text, figures, and data, are free to reuse under a CC BY 4.0 license. </p> <p>The uploaded files contain all data (including raw data), scripts and supporting files used in preparing the manuscript</p> <ul> <li>360panoramas.tar: 360 panoramic pictures taken at the locations at the St Andrews site.</li> <li>PhotosSites.tar: Additional pictures taken at the different ensonification sites</li> <li>ProcessData.tar: This contains all Matlab code for processing and visualizing the data. Also, it contains the templates for all locations as used in the paper.</li> <li>RawData.7z.0xx: These files contain the raw acoustic data as recorded from the microphones for all locations at each of the three sites. This data is provided as Matlab arrays. Due to the file size limitation of Zenodo, the archive has been split into 17 parts. Your archive manager should be able to open all data by accessing RawData.7z.001.</li> <li>Latex.tar: The latex source code and images for the final manuscript.</li> </ul>
SONAR -- experimental redox potentials for organic compounds undergoing 2-electron/2-proton transfer reactions
<p>reference data for the demo-compounds used as input for predicting redox potentials by a trained model </p><p>The file</p><ul><li>lists redox potentials and oxidized/reduced form for organic molecules undergoing a two-electron/two-proton reduction reaction (M + 2 e- + 2 H+ --> MH2)</li><li>contains data for 25 organic compounds compiled from various sources in literature</li><li>uses "|" as a separator</li><li>column names and explanations<ol><li><strong>ID</strong>: abbreviated trivial names e.g. for labelling</li><li><strong>orig redox potential [V]:</strong> original values reported in respective reference</li><li><strong>solvent</strong>: total formula, water (H2O) throughout</li><li><strong>pH</strong>: pH value of electrolyte solution. If not reported, inferred from the concentration of supporting electrolyte</li><li><strong>supporting_electrolyte</strong>: if spefified: total formula, if available; concentration</li><li><strong>SMILES_ox</strong>: molecular structures encoded as (manually assigned) SMILES strings for the oxidized species (M)</li><li><strong>SMILES_red</strong>: molecular structures encoded as (manually assigned) SMILES strings for the reduced species (MH2)</li><li><strong>ref_electrode:</strong> reference electrode the originally reported half cell potential refers to. If not specified, RHE was used as default</li><li><strong>redox potential vs SHE [V]</strong>:<ul><li>In case of missing information, reversible hydrogen electrode (RHE at pH = 0) was assumed, which corresponds to SHE</li><li>In case of conflicting entries (SHE and pH != 0), we assumed the pH should be accounted for and replaced "RHE" as reference electrode instead of "SHE". "NHE" was treated like "RHE".</li><li>In case the reference electrode was other than SHE, NHE or RHE, a respective offset was added. This was the case once for Ag/AgCl (assuming saturated solution, offset = 0.210, see respective reference)</li><li>Finally, the potential values were transferred to SHE according to: E(SHE) = E(RHE) + 0.05913 * pH</li><li>CAVEAT: Lacking information about individual pKa values, no other correction was made.</li></ul></li><li><strong>reference</strong>: orginal source</li></ol></li></ul>
A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining
<p>This dataset serves as an extension to the dataset part of "A convolutional vision transformer for semantic segmentation of side-scan sonar data" published in Ocean Engineering, Volume 86, part 2, 15 October 2023,<strong> </strong>DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0029801823020310">10.1016/j.oceaneng.2023.115647</a> for self-supervised pretraining.</p><p>This dataset consists of patches of side-scan sonar waterfalls collected along the coast of Catalunya during an extensive survey. The waterfalls were partitioned in batches of 384 lines to generate images of size 384 × 384 with a 192 pixel-overlap along-track and across-track. This resulted in a total of 434,164 images capturing various seafloor types including rocky bottoms, sand ripples, detrital funds, posidonia, cymocea, mud, corals, artificial reefs etc.</p><p>Additional tools for using the data for self-supervised pretraining can be found under <a href="https://github.com/DeeperSense/deepersense-seafloorscan">https://github.com/DeeperSense/deepersense-seafloorscan</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected by Tecnoambiente SL as part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020. Project Number: 101016958.</p>
Fusion of Underwater Camera and Multibeam Sonar for Diver Detection and Tracking
<div><strong>Context</strong></div> <div> </div> <div>This dataset is related to previously published public dataset "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments". <a href="../records/7728089">https://zenodo.org/records/7728089</a></div> <div>It contains ZED-right camera and sonar images collected from Hemmoor Lake and DFKI Maritime Exploration Hall.</div> <div> </div> <div>Sensors: Low Frq (1.2MHz) Blueprint Oculus M1200d Sonar and ZED Right Camera</div> <div> </div> <div><strong>Content</strong></div> <div> </div> <div>The dataset is created for Diver Detection and Diver Tracking applications.</div> <div> </div> <div>For the Diver Detection part, the dataset is prepared to train, validate and test YOLOv7 model.</div> <div>7095 images are used for training data, and 3095 images are used for validation data. These sets are augmented from originally captured and sampled ZED camera images. Augmentation methods are not applied to the Test data, which contains 822 images. Train and validation contain images from both the DFKI pool and Hemmor Lake, while the test data is only collected from the lake.</div> <div> </div> <div>To distinguish between the original image and the augmented image, check the name coding. </div> <div>Naming of object detection images:</div> <div>original_image_name.jpg</div> <div>if augmented:</div> <div>original_image_name_<augmentation_number_of_the_same_image>.jpg</div> <div> </div> <div>Object Detection Label Format: </div> <div>YOLO [(class), ((x_min + (x_max - x_min)/2) / image_width), ((y_min + (y_max - y_min)/2) / image_height), ((x_max - x_min) / image_width), ((y_max - y_min) / image_height)]</div> <div> </div> <div>Class: "diver", represented by "0" in object detection labels.</div> <div> </div> <div>Resolution of Object Detection Camera Images: 640x640</div> <div>Resolution of Object Tracking Camera Images: 1280x720</div> <div>Resolution of Object Tracking Low Frequency Sonar: 932x514</div> <div> </div> <div>About the Object Tracking on Sonar, the sampled data is the part where diver moves around the table and the platform. </div> <div>There are 4 cases shared in the dataset, which contain a sonar stream, and corresponding ZED-right camera images. </div> <div>Totally, 1193 points represent the diver on sonar images for the diver tracking application.</div> <div> </div> <div>For the tracking, "tracking_sonar_coordinates_<number>.csv" contains x,y coordinates of a point where the diver is in the sonar image. </div> <div>And "image_sonar_<number>.csv" file contains the matching between sonar and camera images.</div> <div> </div> <div><strong>Acknowledgements</strong></div> <div> </div> <div>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</div> <p> </p>
Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation
<h1>Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation</h1> <h2>Introduction</h2> <p>This is a set of metadata describing a large dataset of synchronized sonar and stereo camera recordings, that were captured between August 2021 and September 2023 during the project <a href="https://robotik.dfki-bremen.de/en/research/projects/deepersense/">DeeperSense</a> (https://robotik.dfki-bremen.de/en/research/projects/deepersense/), as training data for Sonar-to-RGB image translation. <a href="../records/7728089">Parts</a> <a href="../records/10220989">of</a> the sensor data have been published (https://zenodo.org/records/7728089, https://zenodo.org/records/10220989). Due to the size of the sensor data corpus, it is currently impractical to make the entire corpus accessible online. Instead, this metadatabase serves as a relatively compact representation, allowing interested researchers to inspect the data, and select relevant portions for their particular use case, which will be made available on demand. This is an effort to comply with the <a href="https://www.go-fair.org/fair-principles/">FAIR</a> principle A2 (https://www.go-fair.org/fair-principles/) that metadata shall be accessible, even when the base data is not immediately.</p> <h3>Locations and sensors</h3> <p>The sensor data was captured at four different locations, including one laboratory (Maritime Exploration Hall at DFKI RIC Bremen) and three field locations (Chalk Lake Hemmoor, Tank Wash Basin Neu-Ulm, Lake Starnberg). At all locations, a ZED camera and a Blueprint Oculus M1200d sonar were used. Additionally, a SeaVision camera was used at the Maritime Exploration Hall at DFKI RIC Bremen and at the Chalk Lake Hemmoor. The <code>examples/</code> directory holds a typical output image for each sensor at each available location.</p> <h3>Data volume per session</h3> <p>Six data collection sessions were conducted. The table below presents an overview of the amount of data captured in each session:</p> <table> <tbody> <tr> <th>Session dates</th> <th>Location</th> <th>Number of datasets</th> <th>Total duration of datasets [h]</th> <th>Total logfile size [GB]</th> <th>Number of images</th> <th>Total image size [GB]</th> </tr> <tr> <td>2021-08-09 - 2021-08-12</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>52</td> <td>10.8</td> <td>28.8</td> <td>389’047</td> <td>88.1</td> </tr> <tr> <td>2022-02-07 - 2022-02-08</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>35</td> <td>4.4</td> <td>54.1</td> <td>629’626</td> <td>62.3</td> </tr> <tr> <td>2022-04-26 - 2022-04-28</td> <td>Chalk Lake Hemmoor</td> <td>52</td> <td>8.1</td> <td>133.6</td> <td>1’114’281</td> <td>97.8</td> </tr> <tr> <td>2022-06-28 - 2022-06-29</td> <td>Tank Wash Basin Neu-Ulm</td> <td>42</td> <td>6.7</td> <td>144.2</td> <td>824’969</td> <td>26.9</td> </tr> <tr> <td>2023-04-26 - 2023-04-27</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>55</td> <td>7.4</td> <td>141.9</td> <td>739’613</td> <td>9.6</td> </tr> <tr> <td>2023-09-01 - 2023-09-02</td> <td>Lake Starnberg</td> <td>19</td> <td>2.9</td> <td>40.1</td> <td>217’385</td> <td>2.3</td> </tr> <tr> <th> </th> <th> </th> <th>255</th> <th>40.3</th> <th>542.7</th> <th>3’914’921</th> <th>287.0</th> </tr> </tbody> </table> <h2>Data and metadata structure</h2> <h3>Sensor data corpus</h3> <p>The sensor data corpus comprises two processing stages:</p> <ul> <li>raw data streams stored in ROS bagfiles (aka <strong>logfiles</strong>),</li> <li>camera and sonar images (aka <strong>datafiles</strong>) extracted from the logfiles.</li> </ul> <p>The files are stored in a file tree hierarchy which groups them by session, dataset, and modality:</p> <pre><code>${session_key}/ ${dataset_key}/ ${logfile_name} ${modality_key}/ ${datafile_name}</code></pre> <p>A typical logfile path has this form:</p> <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ stereo_camera-zed-2023-09-02-15-06-07.bag</code></pre> <p>A typical datafile path has this form:</p> <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ zed_right/ 1693660038_368077993.jpg</code></pre> <p>All directory and file names, and their particles, are designed to serve as identifiers in the metadatabase. Their formatting, as well as the definitions of all terms, are documented in the file <code>entities.json</code>.</p> <h3>Metadatabase</h3> <p>The metadatabase is provided in two equivalent forms:</p> <ul> <li>as a standalone <a href="https://www.sqlite.org/index.html">SQLite</a> (https://www.sqlite.org/index.html) database file <code>metadata.sqlite</code> for users familiar with SQLite,</li> <li>as a collection of CSV files in the <code>csv/</code> directory for users who prefer other tools.</li> </ul> <p>The database file has been generated from the CSV files, so each database table holds the same information as the corresponding CSV file. In addition, the metadatabase contains a series of convenience views that facilitate access to certain aggregate information.</p> <p>An entity relationship diagram of the metadatabase tables is stored in the file <code>entity_relationship_diagram.png</code>. Each entity, its attributes, and relations are documented in detail in the file <code>entities.json</code></p> <p>Some general design remarks:</p> <ul> <li>For convenience, timestamps are always given in both a human-readable form (ISO 8601 formatted datetime strings with explicit local time zone), and as seconds since the UNIX epoch.</li> <li>In practice, each logfile always contains a single stream, and each stream is stored always in a single logfile. Per database schema however, the entities <code>stream</code> and <code>logfile</code> are modeled separately, with a “many-streams-to-one-logfile” relationship. This design was chosen to be compatible with, and open for, data collections where a single logfile contains multiple streams.</li> <li>A <code>modality</code> is not an attribute of a <code>sensor</code> alone, but of a <code>datafile</code>: Because a <code>sensor</code> is an attribute of a <code>stream</code>, and a single stream may be the source of multiple modalities (e.g. RGB vs. grayscale images from the same camera, or cartesian vs. polar projection of the same sonar output). Conversely, the same modality may originate from different sensors.</li> </ul> <p>As a usage example, the data volume per session which is tabulated at the top of this document, can be extracted from the metadatabase with the following SQL query:</p> <div> <pre><code><span><span>SELECT</span></span> <span> PRINTF(</span> <span> <span>'%s - %s'</span>,</span> <span> <span>SUBSTR</span>(session_start, <span>1</span>, <span>10</span>),</span> <span> <span>SUBSTR</span>(session_end, <span>1</span>, <span>10</span>)) <span>AS</span> <span>'Session dates'</span>,</span> <span> location_name_english <span>AS</span> Location,</span> <span> number_of_datasets <span>AS</span> <span>'Number of datasets'</span>,</span> <span> total_duration_of_datasets_h <span>AS</span> <span>'Total duration of datasets [h]'</span>,</span> <span> total_logfile_size_gb <span>AS</span> <span>'Total logfile size [GB]'</span>,</span> <span> number_of_images <span>AS</span> <span>'Number of images'</span>,</span> <span> total_image_size_gb <span>AS</span> <span>'Total image size [GB]'</span></span> <span><span>FROM</span></span> <span> location</span> <span> <span>JOIN</span> <span>session</span> <span>USING</span> (location_id)</span> <span> <span>JOIN</span> (</span> <span> <span>SELECT</span></span> <span> session_id,</span> <span> <span>COUNT</span>(dataset_id) <span>AS</span> number_of_datasets,</span> <span> <span>ROUND</span>(</span> <span> <span>SUM</span>(dataset_duration) <span>/</span> <span>3600</span>,</span> <span> <span>1</span>) <span>AS</span> total_duration_of_datasets_h,</span> <span> <span>ROUND</span>(</span> <span> <span>SUM</span>(total_logfile_size) <span>/</span> <span>10e9</span>,</span> <span> <span>1</span>) <span>AS</span> total_logfile_size_gb</span> <span> <span>FROM</span></span> <span> location</span> <span> <span>JOIN</span> <span>session</span> <span>USING</span> (location_id)</span> <span> <span>JOIN</span> dataset <span>USING</span> (session_id)</span> <span> <span>JOIN</span> view__dataset_total_logfile_size <span>USING</span> (dataset_id)</span> <span> <span>GROUP</span> <span>BY</span></span> <span> session_id</span> <span> ) <span>USING</span> (session_id)</span> <span> <span>JOIN</span> (</span> <span> <span>SELECT</span></span> <span> session_id,</span> <span> <span>COUNT</span>(datafile_id) <span>AS</span> number_of_images,</span> <span> <span>ROUND</span>(<span>SUM</span>(datafile_size) <span>/</span> <span>10e9</span>, <span>1</span>) <span>AS</span> total_image_size_gb</span> <span> <span>FROM</span></span> <span> <span>session</span></span> <span> <span>JOIN</span> dataset <span>USING</span> (session_id)</span> <span> <span>JOIN</span> stream <span>USING</span> (dataset_id)</span> <span> <span>JOIN</span> <span>datafile</span> <span>USING</span> (stream_id)</span> <span> <span>GROUP</span> <span>BY</span></span> <span> session_id</span> <span> ) <span>USING</span> (session_id)</span> <span><span>ORDER</span> <span>BY</span> session_id;</span></code></pre> </div>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
A Comparison of Recreational and Survey-Grade Side-Scan Sonar Systems in Mapping Reservoir Fish Habitat in 3 Southwest Ohio Reservoirs
Littoral zone aquatic habitat is thought to play an important driver of aquatic organism population dynamics, but historically has been difficult to obtain at the whole waterbody scale because it is costly and time-consuming to collect with traditional aquatic habitat sampling methods. Here we used side-scan sonar to quantification of habitat features over large areas using two levels of equipment: recreational (consumer-grade) and professional (survey-grade). Our goal was to compare performance of the different side-scan sonars by analyzing their ability to map shoreline habitat features (wood, vegetation, and substrate) in three southwest Ohio reservoirs that contain the range of habitat features of interest to fisheries biologists. We used a low-cost Lowrance Active Imaging 3-in-1 system (≈$2,000 USD) recreational sonar and an EdgeTech 6205 system (≈$150,000 USD) survey-grade sonar to collect imagery along the shoreline of three reservoirs in Ohio. Using imagery from each system, We manually delineated patches of submerged woody debris, standing timber, aquatic vegetation, and benthic substrate in GIS. We also compared the size of uniquely identifiable submerged wood from paired imagery to understand potential biases between the systems.
Beneath the Antarctic sea-ice: Fine-scale analysis of Weddell seal (Leptonychotes weddellii) behaviour and predator-prey interactions, using micro-sonar data in Terre Adélie
<p>In this study, we tried to assess:<br>i) whether and how female Weddell seals feed (frequency, depth, duration) during lactation,<br>ii) what is their utilization of a limited foraging area (benthic or pelagic dives) as they are spatially constrained by the presence of their pup, and<br>iii) how can we characterize their foraging dives and the approach/catching phases using new tools providing a more detailed description of their behaviour.<br>Sonar tags were deployed on three breeding female Weddell seals in Terre Adélie (East Antarctica) in November 2019, to study animals' movements and dives at high resolution (3D acceleration, magnetometry, time and depth and GPS location), as well as information on prey and predator-prey interactions using acoustic data.</p>
Side scan sonar backscatter mosaic offshore Thailand, Andaman Sea
<p>The attached mosaics of side scan sonar data were recorded during 3 field campaigns in 2007, 2008 and 2010. High backscatter values are represented by darker colours. The mosaic is georeferenced in EPSG:32647 - WGS 84 / UTM zone 47N.</p> <p>Please refer to Feldens, P.; Schwarzer, K.; Sakuna, D.; Szczuciński, W.; Sompongchaiyakul, P. Sediment distribution on the inner continental shelf off Khao Lak (Thailand) after the 2004 Indian Ocean tsunami. <em>Earth, Planets and Space,</em> 2012, 64, 875-887; DOI:10.5047/eps.2011.09.001 and Sakuna-Schwartz, D.; Feldens, P.; Schwarzer, K.; Khokiattiwong, S.; Stattegger, K. Internal structure of event layers preserved on the Andaman Sea continental shelf, Thailand: tsunami vs. storm and flash-flood deposits. <em>Natural Hazards and Earth System Sciences,</em> 2015, 15, 1181-1199; DOI:10.5194/nhess-15-1181-201 and Feldens, P., Schwarzer K., Sakuna-Schwartz, D., Khokiattiwong, S. (in prep) Offshore geomorphological evolution in Phang Nga province (Thailand) during the Holocene: An example for a sediment starving shelf and references therein for further information on the dataset. </p> <p>The research was funded by Deutsche Forschungsgemeinschaft (DFG) grant No. SCHW 572/11-1 and National Research Council of Thailand (NRCT)</p>
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>
SONAR: A Nursing Activity Dataset with Inertial Sensors
<p>Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities.</p>
Beneath the Antarctic sea-ice: Fine-scale analysis of Weddell seal (Leptonychotes weddellii) behaviour and predator-prey interactions, using micro-sonar data in Terre Adélie
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Direct measurements of 4-dimensional variability in oceanic flow structures with a new towed phased array Doppler sonar
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Behavioral responses of common dolphins to naval sonar
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Estimating spawning Green Sturgeon (Acipenser medirostris Ayres, 1854) abundance using side scan sonar and N-mixture models
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Data from: Sonar estimation of summer chum and pink salmon in the Anvik River, Alaska, 2018
Dual-frequency identification sonar (DIDSON) was used to estimate adult summer chum salmon Oncorhynchus keta and pink salmon O. gorbuscha passage in the Anvik River from June 15 to July 26, 2018. Apportionment to species was determined using data collected from tower counts. A total of 305,098 (SE 5,926) summer chum and 1,122,346 (SE 7,588) pink salmon were estimated to have passed the sonar site. A beach seine sample fishery was conducted to collect age, sex, and length information. Both sonar systems functioned well with minimal interruptions to operation. Range of ensonification was considered adequate for most fish that migrated upstream.
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