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379 results for “submarine”
Evidence of a Vulnerable Marine Ecosystem documented via tourist submarine off Cape Well-Met, Vega Island, Eastern Antarctic Peninsula (Subarea 48.1) - Multimedia
<p>Video evidence of a Vulnerable Marine Ecosystem (VME) was collected via submarine deployed by the tourist super-yacht MY Scenic Eclipse flagged with Malta. The dive was conducted on the 29th November 2019 within Subarea 48.1. The video of this resource supplements the dataset "Evidence of a Vulnerable Marine Ecosystem documented via tourist submarine off Cape Well-Met, Vega Island, Eastern Antarctic Peninsula (Subarea 48.1) - data'' available at <a href="https://ipt.biodiversity.aq/resource?r=cape-well-met_2019">https://ipt.biodiversity.aq/resource?r=cape-well-met_2019</a>.</p> <p>Method step description:</p> <ol> <li> <p>Video evidence of a Vulnerable Marine Ecosystem (VME) was collected via submarine deployed by the tourist super-yacht MY Scenic Eclipse flagged with Malta. Recordings begin at the greatest depth and continue as the submarine travels up the wall. Footage was taken with a GoPro Hero 7 Black mounted in the pilot window of a U-Boat Worx Cruise Sub 7-300<a href="https://www.uboatworx.com/model/cruisesub"> (https://www.uboatworx.com/model/cruisesub).</a> Four submarine dives were filmed.</p> </li> <li> <p>Prior to footage clean-up it was decided that the longest resulting video would be the one that would be analysed. Footage of each of these dives were provided in multiple files.</p> </li> <li> <p>Final Cut Pro X was first used to join the files into one video file per dive.</p> </li> <li> <p>The videos were then cropped to remove the edge of the pilot’s window frame and to adjust the colour balance.</p> </li> <li> <p>Clean-up then followed the same methodology as was used for analyzing the submarine footage for the successful nomination of four VMEs in WG-EMM-18/35 to remove unusable sequences. For the Cape Well-Met footage that meant the removal of any sequences where the submarine was too far from the wall, where the visibility was poor and when the submarine was paused.</p> </li> <li> <p>Footage from Dive C was the longest resulting video after the completion of this clean-up procedure, thus it became the footage that was analysed.</p> </li> </ol> <p>This project is funded by The Soap and The Sea, a Swiss organic and ocean-friendly soap enterprise that donates half of its profits to Ocean Conservation initiatives.</p>
Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - images
<p>This resource contains images that are framegrabs from video recorded by submarine deployed by the MY Arctic Sunrise during their Antarctica expeditions. The first took place in 2018 and focused within the Gerlache Strait and along the western Antarctic Peninsula and the Antarctic Sound in January 2018. Dives were conducted beginning 19th to 27th January 2018. This resource supplement the images for “Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - data”</p>
SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization
<h1><strong>Abstract</strong></h1> <p>This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. <br><br>SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms.<br>To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available <a href="https://github.com/remaro-network/SubPipe-dataset">online.</a></p> <p>On Zenodo we provide <em>three</em> versions for SubPipe. One is the full version (<strong>SubPipe.zip</strong>, ~80GB unzipped) and two subsamples: <strong>SubPipeMini.zip</strong>, ~12GB unzipped and <strong>SubPipeMini2.zip</strong>, ~16GB unzipped. Both subsamples are only parts of the entire dataset (SubPipe.zip). SubPipeMini is a subset, containing semantic segmentation data, and it has interesting camera data of the underwater pipeline. On the other hand, SubPipeMini2 is mainly focused on underwater side-scan sonar images of the seabed including ground truth object detection bounding boxes of the pipeline.</p> <p><strong>For (re-)using/publishing SubPipe, please include the following copyright text:</strong></p> <p><em><strong>SubPipe</strong> is a public dataset of a submarine outfall pipeline, property of Oceanscan-MST. This dataset was acquired with a Light Autonomous Underwater Vehicle by Oceanscan-MST, within the scope of Challenge Camp 1 of the</em> <em>H2020 </em><a href="https://remaro.eu/"><em>REMARO</em></a><em> project.</em></p> <p><em>More information about OceanScan-MST can be found at </em><a href="https://www.oceanscan-mst.com/"><em>this link</em></a><em>.</em></p> <h1><strong>Cam0 — GoPro Hero 10</strong></h1> <h4>Camera parameters:</h4> <ul> <li>Resolution: 1520×2704</li> <li>fx = 1612.36</li> <li>fy = 1622.56</li> <li>cx = 1365.43</li> <li>cy = 741.27</li> <li>k1,k2, p1, p2 = [−0.247, 0.0869, −0.006, 0.001]</li> </ul> <h1><strong>Side-scan Sonars</strong></h1> <p>Each sonar image was created after 20 “ping” (after every 20 new lines) which corresponds to approx. ~1 image / second.</p> <p>Regarding the object detection annotations, we provide both COCO and YOLO formats for each annotation. A single COCO annotation file is provided per each chunk and per each frequency (low frequency vs. high frequency), whereas the YOLO annotations are provided for each SSS image file.</p> <p>Metadata about the side-scan sonar images contained in this dataset:</p> <table> <tbody> <tr> <td><strong>Images for object detection</strong></td> <td> </td> </tr> <tr> <td># Low Frequency (LF):</td> <td> 5000</td> </tr> <tr> <td>LF image size:</td> <td>2500 × 500</td> </tr> <tr> <td># High Frequency (HF):</td> <td> 5030</td> </tr> <tr> <td>HF Image size</td> <td>5000 × 500</td> </tr> <tr> <td><strong>Total number of images:</strong></td> <td>10030</td> </tr> <tr> <td><strong>Annotations</strong><strong><br></strong></td> <td> </td> </tr> <tr> <td># Low Frequency:</td> <td> 3163</td> </tr> <tr> <td># High Frequency:</td> <td> 3172</td> </tr> <tr> <td><strong>Total number of annotations:</strong></td> <td><strong> </strong>6335</td> </tr> </tbody> </table>
Indicative distribution map for Ecosystem Functional Group M3.2 Submarine canyons
<p>This archive contains indicative distribution maps and profiles for <strong>M3.2 Submarine canyons</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Output files corresponding to "Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to the output files that were produced for the study reported in:</p> <ul> <li>Sawyer, Audrey H., Cédric H. David, and James S. Famiglietti, (2016), Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities, Science, 353(6300), 705-707. DOI:10.1126/science.aag1058. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Regions used are: Northeast (NE: 01), Mid-Atlantic (MA: 02), South-Atlantic North (SAN: 03N), South-Atlantic South (SAS: 03S), South-Atlantic West (SAW: 03W), Lower Mississippi (MS: 08), Texas (TX: 12), California (CA: 18), and Pacific Northwest (NW: 17).</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2010 Census dataset (CENSUS 2010), obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>NHDFlowline_CONUS_coastline.zip. </em>This zip file contains a shapefile with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in regions used. </li> <li><em>Catchment_CONUS_coastline.zip. </em>This zip file contains a shapefile with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in regions used. </li> <li><em>Catchment_CONUS_coastline_centroid.zip</em>.<em> </em>This zip file contains a shapefile with the centroids of the above catchments. </li> <li><em>SGD_Coastal_Vulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID. Unique feature identifier in NHDPlusV2 (-).</li> <li>LENGTHkm. Length of coastline feature (km).</li> <li>REACHCODE. Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature (-).</li> <li>AREAsqkm. Area of coastal catchment feature (km<sup>2</sup>).</li> <li>REGION. NHDPlusV2 region: NE = Northeast, MA = Mid-Atlantic, SAN = South Atlantic North, SAS = South Atlantic South, SAW = South Atlantic West, TX = Texas, MS = Lower Mississippi, CA = California, PN = Pacific Northwest (-).</li> <li>RLENGTHkm. Total length of coastline accumulated by REACHCODE (km).</li> <li>RAREAsqkm. Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>BGRUNkgpsqm. Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>SGDsqmpy. Average annual fresh SGD rate for REACHCODE (m<sup>2</sup>/y).</li> <li>RCOUNT. Number of features by REACHCODE (-).</li> <li>PDENpsqkm. Population density for coastal catchment feature (km<sup>-2</sup>).</li> <li>SWIVULN. Vulnerability to saltwater intrusion: - 1 = vulnerable, 0 = not vulnerable (-).</li> <li>PCTDEV11. Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>CONTVULN. Vulnerability to offshore contamination associated with direct groundwater discharge: - 1 = vulnerable, 0 = not vulnerable (-).</li> </ul> </li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>No bugs have been unveiled since publication of this dataset or the associated manuscript. Vulnerability thresholds are subjective and could be adjusted for different applications, refer to published manuscript for approaches used here.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by the Ohio State University School of Earth Sciences, and NSF grant EAR-1446724 (A.H.S); the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and grants from the NASA SWOT and Sea Level Science Teams (C.H.D. and J.S.F.).</p>
IFC Submarine Interconnection Projects Data Model
<p>Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.</p>
Global Airborne Observatory: Submarine Groundwater Discharge on West Hawaii Island
<p>Mapped submarine groundwater discharge (SGD) for the west coast of Hawaii Island. A full description of the data source and methodology is available at:</p> <p>Asner, G.P., N.R. Vaughn, and J. Heckler. 2024. Operational mapping of submarine groundwater discharge into coral reefs: Application to West Hawaii Island. Oceans 5, 547-559. https://doi.org/10.3390/oceans5030031</p> <p>There are two data layers:</p> <ol> <li>Estimated point sources of SGD</li> <li>Estimated dischage areas of SGD </li> </ol> <p>SGD discharge point sources as a point layer and SGD discharge areas are provided as polygon layers, both in GeoJSON format. The point source layer includes a Field LocCertainty, describing the certainty in percent that the discharge location is correctly identified. The discharge area layer includes fields InsideC and OutsideC, which average thermal sensor temperature in Celsius inside and outside, respectively, as well as fields for the temperature difference (dTemp) and the size of the dicharge area in hectares (ha). Both layers use the WGS84 coordinate system with spatial coordinates giving positions as degrees longitiude and latitude.</p>
Self-supervised learning of seismological data reveals undocumented eruptive sequences at the Mayotte submarine volcano - Supplementary Materials
<p>The following files are shared:<br> - The scripts used to train the model and generate the figures of the article<br> - The input images used to train the model as well as the final outputs (embedding matrix and the associated filenames matrix)<br> - The clusters organization with their associated images</p>
Fig. 8 in Umbellula pomona sp. nov., a new sea pen from Mar del Plata Submarine Canyon (Cnidaria: Octocorallia: Pennatulacea)
Fig. 8. General aspect of the unique specimen of a juvenile-like paratype of Umbellula pomona Risaro, Williams & Lauretta sp. nov. (MACN-IN 42609, paratype C). Abbreviations: CP = central polyp; LP = lateral polyp; R = rachis; PD = peduncle; T = tentacles.
Fig. 7 in Umbellula pomona sp. nov., a new sea pen from Mar del Plata Submarine Canyon (Cnidaria: Octocorallia: Pennatulacea)
Fig. 7. Variability of sizes and ornamentations of the pinnules' sclerites of the holotype of Umbellula pomona Risaro, Williams & Lauretta sp. nov. (MACN-IN 42608).
Fig. 2. A in Umbellula pomona sp. nov., a new sea pen from Mar del Plata Submarine Canyon (Cnidaria: Octocorallia: Pennatulacea)
Fig. 2. A. General aspect of Umbellula pomona Risaro, Williams & Lauretta sp. nov. A. Holotype (MACN-IN 42608). B. Detail of the terminal cluster of paratype A (MACN-IN 42609), showing three autozooids that form the terminal cluster and amplifications of sclerites (up) and siphonozooids (down). Abbreviations: CP = central polyp; LP = lateral polyp; Pd = peduncle; R = rachis; T = tentacle; S = siphonozooids; Scl = sclerites.
A legacy of submarine slope failure in seismic reflection data along the active Hikurangi Margin, Aotearoa New Zealand
<p><span>We present a database that documents mass transport deposits (MTDs) in 32 marine geophysical surveys, encompassing >38,000 line-km of 2D seismic profiles. We map and characterise 737 MTDs, showing variations in size, location and style of failure, which we attribute to changes in geomorphic setting from north to south. MTDs in the northern Hikurangi margin, characterised by a high taper wedge and seamount subduction, show a broad range in size, with the highest proportion of MTDs displaying blocky or intact internal architecture. The central margin, characterised by lower wedge taper, hosts the most MTDs (51%), albeit with the thinnest (on average) and clustering within interridge basins. The southern Hikurangi margin hosts widespread submarine canyons and the largest (on average) MTDs, based on area and thickness. We demonstrate the importance of seismic archives in providing new insights into MTD preservation and discuss the bias between seafloor geomorphology and subseafloor seismic data in quantifying MTD occurrence. Our findings support the interrogation of the varied and complex causes of submarine landslides along active margins generally, as well as regions prone to cascading geohazards and landslide-induced tsunami. </span></p>
Vapor bubbles and velocity control on the cooling rates of lava and pyroclasts during submarine eruptions
<p>These dataset contains videos (.mp4) of experiments and the time-temperature data (.txt) measured during the experiments. Each video starts when the sample is completely submerged in the water. The temperature data represents the full experimental duration, where a spike in water temperature data to about 27 deg C on average indicates the synchronization process between the video and the data. </p>
Dataset for the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings"
<p>The results of the analysis in the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings" published in Geophysical Research Letters are available here. For the details of the file, please see Readme.pdf.</p>
Fig. 13 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 13: The effect of hydrodynamics inside the Y-Cave, the location behind section B-B' (Fig. 2) at 5.5 m of depth, where the unusually coloured sediment sample was collected for analysis.
Fig. 12 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 12: Limestone tablets from the three representative sites after the 1-year exposure period: A) with bioaccumulation at site 1; B) corroded at site 3; C) abraded at site 6 (Fig. 3, Tab. 1).
Fig. 11 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 11: Cave features: A) stalactites in the chamber with the air pocket (section C-C'); B) submerged stalagmites and flowstones with a lack of marine cave biota (section C-C'); C) submerged scallops (asymmetrical, cuspate, oyster-shell-shaped dissolution depressions in the cave walls used as an indicator of flow direction; Murphy, 2012), (section D-D'); D) corroded cave walls (section F-F').
Fig. 5 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 5: The annual variation of temperature along the Y-Cave (from August 23–27, 2003 to July 4/October 8, 2004). Measurement positions are given in Fig. 3 and depths in Table 1.
Fig. 10 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 10: The mass (m.f.) and volume fractions (v.f.) of four sediment categories in the sediment sample collected behind section B-B' (Fig. 2), at 5.5 m of depth inside the Y-Cave; A) detrital terrigenous sediment>4 mm, B) mixed biogenic and terrigenous detritus, C) shells of gastropod Homalopoma sanguineum, D) other biogenic material – shells, tests and skeletons of other marine organisms.
Fig. 4 in Biological response to geochemical and hydrological processes in a shallow submarine cave
Fig. 4: Living communities inside the Y-Cave: A) the entrance part of the cave, vertical wall, depth 9 m, biocenosis of semi-dark caves (GSO, see text for explanation of acronym) dominated by numerous sponge species; B) the entrance part of the cave, ceiling, depth 6 m, GSO dominated by scleractinian coral Leptopsammia pruvoti; C) the entrance part of the cave, overhang, depth 7 m, GSO dominated by scleractinian coral Madracis pharensis; D) the entrance part of the cave, vertical wall (near the bottom), depth 9 m, GSO, a large specimen of the orange sponge Agelas oroides dominates the photo; E) the middle part of the cave, in front of the section C-C', bottom, depth 10 m, a massive white specimen of the sponge Chondrosia reniformis; F) the middle part of the cave, between sections C-C' and D-D', vertical wall and overhang, depth 5 m, the transition from GSO to biocenosis of caves and ducts in total darkness (GO, see text for explanation of acronym), the community is dominated by serpulids; G) the middle part of the cave, between sections C-C' and D-D', vertical wall, depth 5 m, transition from GSO to GO, a dense population of brachiopod Novocrania anomala, encrusting sponge Placospongia decorticans and serpulids; H) the end part of the cave, near the section G-G', vertical wall with overhang and horizontal shelf, depth 6 m, GO with scarce calcareous sponges and serpulids (see Fig. 2 for position of the sections).
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International Brain Laboratory public data
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OpenNeuro
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