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40 results for “Marine imaging”
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>
Dataset for marine vessel detection from Sentinel 2 images in the Finnish coast
<p>This dataset contains annotated marine vessels from 15 different Sentinel-2 product, used for training object detection models for marine vessel detection. The vessels are annotated as bounding boxes, covering also some amount of the wake, if present.</p> <h2>Source data</h2> <div> <div>Individual products used to generate annotations are shown in the following table:</div> </div> <div> </div> <div> <table style="width: 58.034%; height: 411.47px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"><strong>Location</strong></td> <td style="width: 79.3617%; height: 19.5938px;"><strong>Product name</strong></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Archipelago sea</td> <td style="width: 79.3617%; height: 39.1875px;">S2A_MSIL1C_20220515T100031_N0510_R122_T34VEM_20240617T162344.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220619T100029_N0510_R122_T34VEM_20240627T204751.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220721T095041_N0510_R079_T34VEM_20240712T224506.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220813T095601_N0510_R122_T34VEM_20240717T115958.SAFE</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Gulf of Finland</td> <td style="width: 79.3617%; height: 39.1875px;">S2B_MSIL1C_20220606T095029_N0510_R079_T35VLG_20240619T111429.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220626T095039_N0510_R079_T35VLG_20240620T013500.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220703T094039_N0510_R036_T35VLG_20240702T075354.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220721T095041_N0510_R079_T35VLG_20240712T224506.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Bay</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220627T100611_N0510_R022_T34WFT_20240628T041908.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220712T100559_N0510_R022_T34WFT_20240718T033027.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220828T095549_N0510_R122_T34WFT_20240708T035231.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Sea</td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220619T100029_N0510_R122_T34VEN_20240627T204751.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220624T100041_N0510_R122_T34VEN_20240714T110124.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220813T095601_N0510_R122_T34VEN_20240717T115958.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Kvarken</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220617T100611_N0510_R022_T34VER_20240627T094433.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220712T100559_N0510_R022_T34VER_20240718T033027.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220826T100611_N0510_R022_T34VER_20240705T062429.SAFE</td> </tr> </tbody> </table> </div> <div> <div> </div> <div>Even though the reference data IDs are for L1C products, L2A products from the same acquisition dates can be used along with the annotations. However, Sen2Cor has been known to produce incorrect reflectance values for water bodies.</div> <div> </div> <div>The corresponding L2A product identifiers are:</div> </div> <div> </div> <div> <table style="width: 58.034%; height: 411.47px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"><strong>Location</strong></td> <td style="width: 79.3617%; height: 19.5938px;"><strong>Product name</strong></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Archipelago sea</td> <td style="width: 79.3617%; height: 39.1875px;">S2A_MSIL2A_20220515T100031_N0400_R122_T34VEM_20220515T141508.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220619T100029_N0510_R122_T34VEM_20240628T011619.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220721T095041_N0510_R079_T34VEM_20240713T035445.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220813T095601_N0510_R122_T34VEM_20240717T165127.SAFE</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Gulf of Finland</td> <td style="width: 79.3617%; height: 39.1875px;">S2B_MSIL2A_20220606T095029_N0510_R079_T35VLG_20240619T162121.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220626T095039_N0510_R079_T35VLG_20240620T063951.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220703T094039_N0510_R036_T35VLG_20240702T130032.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220721T095041_N0510_R079_T35VLG_20240713T035445.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Bay</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220627T100611_N0510_R022_T34WFT_20240628T095704.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220712T100559_N0510_R022_T34WFT_20240718T063657.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220828T095549_N0510_R122_T34WFT_20240708T091048.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Sea</td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20210714T100029_N0500_R122_T34VEN_20230224T182455.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220619T100029_N0510_R122_T34VEN_20240628T011619.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220624T100041_N0510_R122_T34VEN_20240714T162313.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220813T095601_N0510_R122_T34VEN_20240717T165127.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Kvarken</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220617T100611_N0510_R022_T34VER_20240627T130404.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220712T100559_N0510_R022_T34VER_20240718T063657.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220826T100611_N0510_R022_T34VER_20240705T120522.SAFE</td> </tr> </tbody> </table> </div> <div><br> <div>The raw products can be acquired from <a href="https://dataspace.copernicus.eu" target="_blank" rel="noopener">Copernicus Data Space Ecosystem.</a> The products listed above can be unavailable due to e.g. processing level updates and old versions being deleted. In those cases, try searching with the tile identifier and acquisition date in order to get the correct product ID.</div> <br> <h2>Annotations</h2> <br> <div>The annotations are bounding boxes drawn around marine vessels so that some amount of their wakes, if present, are also contained within the boxes. The data are distributed as geopackage files, so that one geopackage corresponds to a single Sentinel-2 tile, and each package has separate layers for individual products as shown below:</div> <br> <blockquote> <div>T34VEM</div> <div>|-20220515</div> <div>|-20220619</div> <div>|-20220721</div> <div>|-20220813</div> </blockquote> <br> <div>All layers have a column <strong>id</strong>, which has the value <strong>b</strong><strong>oat</strong> for all annotations.</div> <br> <div>CRS is EPSG:32634 for all products except for the Gulf of Finland (35VLG), which is in EPSG:32635. This is done in order to have the bounding boxes to be aligned with the pixels in the imagery.</div> <br> <div>As tiles 34VEM and 34VEN have an overlap of 9.5x100 km, 34VEN is not annotated from the overlapping part to prevent data leakage between splits.</div> <br> <h3>Annotation process</h3> The minimum size for an object to be considered as a potential marine vessel was set to 2x2 pixels. Three separate acquisitions for each location were used to detect smallest objects, so that if an object was located at the same place in all images, then it was left unannotated. The data were annotated by two experts. <div> </div> <table style="width: 63.327%; height: 391.876px;"> <tbody> <tr style="height: 39.1875px;"> <td style="width: 72.7285%; height: 39.1875px;"><strong>Product name</strong></td> <td style="width: 23.0224%; height: 39.1875px;"><strong>Number of annotations</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220515T100031_N0510_R122_T34VEM_20240617T162344.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">183</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220619T100029_N0510_R122_T34VEM_20240627T204751.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">519</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220721T095041_N0510_R079_T34VEM_20240712T224506.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1518</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220813T095601_N0510_R122_T34VEM_20240717T115958.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1371</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220606T095029_N0510_R079_T35VLG_20240619T111429.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">277</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220626T095039_N0510_R079_T35VLG_20240620T013500.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1205</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220703T094039_N0510_R036_T35VLG_20240702T075354.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">746</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220721T095041_N0510_R079_T35VLG_20240712T224506.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">971</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220627T100611_N0510_R022_T34WFT_20240628T041908.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">122</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220712T100559_N0510_R022_T34WFT_20240718T033027.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">162</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220828T095549_N0510_R122_T34WFT_20240708T035231.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">98</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">450</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220619T100029_N0510_R122_T34VEN_20240627T204751.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">66</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220624T100041_N0510_R122_T34VEN_20240714T110124.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">424</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220813T095601_N0510_R122_T34VEN_20240717T115958.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">399</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220617T100611_N0510_R022_T34VER_20240627T094433.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">83</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220712T100559_N0510_R022_T34VER_20240718T033027.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">184</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;">S2A_MSIL1C_20220826T100611_N0510_R022_T34VER_20240705T062429.SAFE</td> <td style="width: 23.0224%; height: 19.5938px;">88</td> </tr> </tbody> </table> <br><br> <h3>Annotation statistics</h3> <br>Sentinel-2 images have spatial resolution of 10 m, so below statistics can be converted to pixel sizes by dividing them by 10 (diameter) or 100 (area).</div> <div> <table> <tbody> <tr> <td> </td> <td><strong>mean</strong></td> <td><strong>min</strong></td> <td><strong>25%</strong></td> <td><strong>50%</strong></td> <td><strong>75%</strong></td> <td><strong>max</strong></td> </tr> <tr> <td><strong>Area (m²)</strong></td> <td>5305.7</td> <td>567.9</td> <td>1629.9</td> <td>2328.2</td> <td>5176.3</td> <td>414795.7</td> </tr> <tr> <td><strong>Diameter (m)</strong></td> <td>92.5</td> <td>33.9</td> <td>57.9</td> <td>69.4</td> <td>108.3</td> <td>913.9</td> </tr> </tbody> </table> <br><br> <div>As most of the annotations cover also most of the wake of the marine vessel, the bounding boxes are significantly larger than a typical boat. There are a few annotations larger than 100 000 m², which are either cruise or cargo ships that are travelling along ordinal directions instead of cardinal directions, instead of e.g. smaller leisure boats.</div> <br> <div>Annotations typically have diameter less than 100 meters, and the largest diameters correspond to similar instances than the largest bounding box areas.</div> <br> <h3>Train-test-split</h3> <br> <div>We used tiles 34VEN and 34VER as the test dataset. For validation, we split the other three tile areas into 5x5 equal sized grid, and used 20 % of the area (i.e 5 cells) for the validation. The same split also makes it possible to do cross-validation.</div> <div> </div> <div> </div> <div> </div> </div> <div> <h3>Post-processing</h3> </div> <div><br> <div>Before evaluating, the predictions for the test set are cleaned using the following steps:</div> <br> <div>1. All prediction whose centroid points are not located on water are discarded. The water mask used contains layers `jarvi` (Lakes), `meri` (Sea) and `virtavesialue` (Rivers as polygon geometry) from the Topographical database by the National Land Survey of Finland. Unfortunately this also discards all points not within the Finnish borders.</div> <div>2. All predictions whose centroid points are located on water rock areas are discarded. The mask is the layer `vesikivikko` (Water rock areas) from the Topographical database.</div> <div>3. All predictions that contain an above water rock within the bounding box are discarded. The mask contains classes `38511`, `38512`, `38513` from the layer `vesikivi` in the Topographical database.</div> <div>4. All predictions that contain a lighthouse or a sector light within the bounding box are discarded. Lighthouses and sector lights come from Väylävirasto data, `ty_njr` class ids are 1, 2, 3, 4, 5, 8</div> <div>5. All predictions that are wind turbines, found in Topographical database layer `tuulivoimalat`</div> <div>6. All predictions that are obviously too large are discarded. The prediction is defined to be "too large" if either of its edges is longer than 750 meters.</div> </div> <div> </div> <div>Model checkpoint for the best performing model is available on Hugging Face platform: <a href="https://huggingface.co/mayrajeo/marine-vessel-detection-yolov8">https://huggingface.co/mayrajeo/marine-vessel-detection-yolo</a><br> <h2>Usage</h2> The simplest way to chip the rasters into suitable format and convert the data to COCO or YOLO formats is to use <a href="https://github.com/mayrajeo/geo2ml">geo2ml</a>. First download the raw mosaics and convert them into GeoTiff files and then use the following to generate the datasets. <div> </div> To generate COCO format dataset run</div> <div> </div> <div> <pre><code>from geo2ml.scripts.data import create_coco_dataset raster_path = '<path_to_raster>' outpath = '<path_to_save_the_dataset>' poly_path = '<path_to_gpkg>' layer = '<date_of_raster>' create_coco_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, dataset_name='<name_of_dataset>', gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)</code></pre> </div> <div><br> <div>To generate YOLO format dataset run</div> <div> <pre><code>from geo2ml.scripts.data import create_yolo_dataset raster_path = '<path_to_raster>' outpath = '<path_to_save_the_dataset>' poly_path = '<path_to_gpkg>' layer = '<date_of_raster>' create_yolo_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)</code></pre> </div> </div>
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth.
Figure 2 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 2. – Three images of organisms obtained by cropping images of lots; from left to right: Chalinidae (Porifera), Polyclinidae (Chordata), Hormatidae (Cnidaria).
Figure 3 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 3. – Image of a batch of macro-invertebrate bycatch organisms from Kerguelen Exclusive Economical Zone (Poker 4 survey, 2017), including corals, a crinoïd, an ophiurid, a sea urchin and a brachiopoda; organisms are incomplete and have been quickly spread out over a small plate to take the picture.
Figure 6 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 6. – Example of detection and classification obtained with an image including an Ophiuroid, a piece of coral and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 5 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 5. – Example of detection and classification obtained with an image including Ascidians and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Text-fig. 18. Stereo images of fossil marine gastropod steinkerns from White Patch Fossil Site 1. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 18. Stereo images of fossil marine gastropod steinkerns from White Patch Fossil Site 1.
Freshwater and Marine Image Bank, University Libraries, U Washington: Freshwater and Marine Image Bank, U Washington
The Freshwater and Marine Image Bank is a digital collection from the University of Washington Libraries of images related to freshwater and marine topics, in all their diversity. It includes images of fish, shellfish, and marine mammals, pictures of fish hatcheries and dams and vessels, materials related to polar exploration, regional and traditional fisheries, and limnological (freshwater) subjects. Its scope is global. <p></p>http://content.lib.washington.edu/fishweb/index.html<p></p>The Freshwater and Marine Image Bank is a digital collection from the University of Washington Libraries of images related to freshwater and marine topics, in all their diversity. It includes images of fish, shellfish, and marine mammals, pictures of fish hatcheries and dams and vessels, materials related to polar exploration, regional and traditional fisheries, and limnological (freshwater) subjects. Its scope is global. <p></p>http://content.lib.washington.edu/fishweb/index.html
Freshwater and Marine Image Bank, University Libraries, U Washington: Original zip resource
The Freshwater and Marine Image Bank is a digital collection from the University of Washington Libraries of images related to freshwater and marine topics, in all their diversity. It includes images of fish, shellfish, and marine mammals, pictures of fish hatcheries and dams and vessels, materials related to polar exploration, regional and traditional fisheries, and limnological (freshwater) subjects. Its scope is global. <p></p>http://content.lib.washington.edu/fishweb/index.html<p></p>
DeepPlastic: An Open Source Image Dataset for Epipelagic Marine Plastic Detection
<p>Deep Plastic</p> <ul> <li>Enhanced Object Detection for Epipelagic Plastic.</li> <li>This repository contains source code for the method developed in <a href="https://arxiv.org/pdf/2105.01882.pdf">DeepPlastic: Identifying Marine Plastic In The Epipelagic Zone using Computer Vision and Deep Learning</a></li> <li> <p>Information:</p> </li> <li>Paper: [Coming Soon]</li> <li>YouTube video of Results: <a href="https://youtu.be/8zBdFxaK4Os">https://youtu.be/8zBdFxaK4Os</a></li> <li> <p>Object Detection Model</p> </li> <li>Four models: YOLOv4, YOLOv5, MobileSSD, Faster RCNN Inception V2</li> <li>Small efficient and high precision models can be used for real-time object detection.</li> <li>Model architecture and implementation details: <a href="https://arxiv.org/">https://arxiv.org/</a></li> <li>Weights for YOLOv4 and YOLOv5 are provided in the model/ <ul> <li>YOLOv4: best. weights; use <a href="https://drive.google.com/file/d/1YOTtZ2cHbqgxHukzLp01OVsUoa2CwwXs/view?usp=sharing">best.weights</a></li> <li>YOLOv5: best.pt; use <a href="https://drive.google.com/file/d/14mBOhtLrE2d3hudqjwBZmawKAvTF4zxS/view?usp=sharing">best.pt</a></li> </ul> </li> <li> <p>Google Colab Links</p> <p>Note: Click on File and Save Copy in Drive. If you try to edit my file it'll ask you for permission and send me an email. Please make your own copy.</p> </li> <li>YOLOv5: <a href="https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing">https://colab.research.google.com/drive/1_qzbpBWkNfxQ0ny-DvsKicCeM0aFU4eW?usp=sharing</a></li> <li> <p>DeepTrash DataSet</p> </li> <li>1900 training images, 637 test images, 637 validation images (60, 20, 20 split)</li> <li>Field images taken from Lake Tahoe, San Francisco Bay and Bodega Bay in CA.</li> <li>Deep Sea images are from JAMSTEK JEDI dataset: <a href="http://www.godac.jamstec.go.jp/">http://www.godac.jamstec.go.jp/</a></li> </ul>
Data from: Salty Sensors, Fresh Ideas: The use of molecular and imaging sensors in understanding plankton dynamics across marine and freshwater ecosystems
Open the record for dataset details and reuse information.
Table A6 images from Computer-aided drug design (CADD) to de-orphanise marine molecules: Finding potential therapeutic agents for neurodegenerative and cardiovascular diseases
<p>High Reslution Images from Table A6</p>
Fig. 1 a –d Marine benthic organisms used for bio-optical measurements. a Boneccia viridis, b Isodictya pacmata, c Hymedesmia paupertas, d in Development of hyperspectral imaging as a bio-optical taxonomic tool for pigmented marine organisms
Fig. 1 a –d Marine benthic organisms used for bio-optical measurements. a Boneccia viridis, b Isodictya pacmata, c Hymedesmia paupertas, d Hymedesmia sp.
Marine snow surface production and bathypelagic export at the Equatorial Atlantic from an imaging float
<p>Raw dataset used for paper submitted to Biogeoscience journal</p>
FIGURES 17–19. Hassallia littoralis TEM images. Fig. 17. Hormogonia formation through a necridic cell. Fig. 18. Young isopolar hormogonium with a wide stratified sheath. Fig. 19 in Hassallia littoralis sp. nov. (Cyanobacteria, Microchaetaceae) from Mexico's marine supralittoral based on morphological and molecular evidence
FIGURES 17–19. Hassallia littoralis TEM images. Fig. 17. Hormogonia formation through a necridic cell. Fig. 18. Young isopolar hormogonium with a wide stratified sheath. Fig. 19. Evident polar nodule in a basal heterocyte. Scale bars: Fig. 17: 3 µm, Fig. 18: 5 µm, Fig. 19: 1 µm.
Supplementary material 1 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Discussion question response data
Figure 2 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Figure 2 Polling results of discussion session 1 "Extent of imaging", question 1: "What are the barriers to increasing the extent of imaging the oceans?".
Figure 9 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Figure 9 Polling results of discussion session 3 "Automation", question 3: "What do you think is the biggest barrier to making AI systems applicable across datasets?".
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.