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10 results for “Floating litter”
Fig. 2 in Second floor, please: the fish fauna of floating litter banks in Amazonian streams and rivers
Fig. 2. Rarefaction curve (Mao Tau Index) based on the number of samples collected in floating litter banks in four river basins in the Brazilian Amazon (Copacá, Cuieiras, Preto da Eva, and Urubu rivers). (a) All floating litter banks recorded, and (b) disentangling rarefaction models for each river basin.
Fig. 1 in Second floor, please: the fish fauna of floating litter banks in Amazonian streams and rivers
Fig. 1. Floating litter banks of different dimensions and means of accumulation (i.e., diverse retention mechanisms): (a) floating litter banks retained by branches of riparian vegetation; (b) a large floating litter bank retained in a "ria lake" condition at the confluence of the Aliança Stream with the Branco River; (c) a small floating litter bank close to the margin of a 4th-order stream in the Urubu River basin; and (d) a large floating litter bank almost completely out of the water in a stream in the Urubu River basin during the dry season.
Fig. 3 in Second floor, please: the fish fauna of floating litter banks in Amazonian streams and rivers
Fig. 3. The fish fauna associated with kinon banks is dominated by species of characins (Characiformes), catfishes (Siluriformes) and electric knife fishes (Gymnotiformes). Some of the more common and abundant species in our samples were the following: (a) Elachocharax pulcher, Crenuchidae; (b) Hypopygus lepturus, Hypopomidae; (c) Batrochoglanis raninus, Pseudopimelodidae; and (d) Brachyglanis microphthalmus, Heptapteridae.
TUD-GV Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia T, Vallendar AJ, de Vries R, Kapelan Z and Taormina R (2023) Advancing deep learning-based detection of floating litter using a novel open dataset. <em>Front. Water</em> 5:1298465. doi: 10.3389/frwa.2023.1298465.</p> <p>The large-scale "TU Delft - Green Village" (TUD-GV) dataset is for detecting floating litter with computer vision. We created this dataset from experiments conducted during 10 days in February and April 2021 in a small drainage canal at The Green Village — a field lab facility in the TU Delft Campus, the Netherlands. We captured data using two action cameras (GoPro HERO4 and GoPro MAX 360) and a phone (Huawei P30 Pro) mounted on four different locations on a bridge. All devices recorded videos with a resolution of 1080p, a linear field of view, and a FPS (frame per second) of 24 (for the action cameras) or 30 (for the phone).</p> <p>This dataset consists of 9,473 RGB images. We manually labeled the images in the TUD-GV dataset into four classes: <em>no litter</em> (0 items), <em>little litter</em> (1-2 items), <em>moderate litter</em> (3-5 items), and <em>lots of litter</em> (6-10 items) according to the number of litter items in images.</p> <p>This dataset is stored in the ZIP file contain 77 directories and the TUD-GV.xls file. Each of these directories contain four class label directories, and each label directory contains JPG images. The TUD-GV.xls file contains the detailed information of images in 77 directories, including collecting date, collecting time, device, device location (in a bridge), device degree, device height, weather conditions, litter source, the number of images per class, and the number of images in total.</p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <p>@article{jia2023advancing, title={Advancing Deep Learning-based Detection of Floating Litter using a Novel Open Dataset}, author={Jia, Tianlong and Vallendar, Andre Jehan and de Vries, Rinze and Kapelan, Zoran and Taormina, Riccardo}, journal={Frontiers in Water}, volume={5}, pages={1298465}, publisher={Frontiers}, year={2023} }</p>
TUD-GV Dataset for Floating Litter Detection (object detection task)
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>This dataset is a subset of the large-scale "TU Delft - Green Village" (TUD-GV), which includes 9,473 RGB images. More details on the TUD-GV dataset can be found at: <a href="https://doi.org/10.5281/zenodo.7636124" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7636124</a>. This subset used in this publication consists of 1,501 images, selected from the full TUD-GV dataset. All floating litter items in this subset have been annotated with bounding boxes. This subset is specifically for detecting floating litter in object detection tasks.</p> <p>The 1,501 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file.</p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Amsterdam Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>The Amsterdam dataset is for detecting floating litter with computer vision. We created the Amsterdam dataset from one experiment conducted on 1st March 2023, in canals and ponds at Amsterdam, the Netherlands. We recorded images using an action camera (GoPro Hero 10). All images used in this publication are captured by the device positioned at a distance of maximum 2 m from the water surface. The image resolution is 5568*4176. This dataset consists of 9 RGB images. We manually labeled the litter items in these images with bounding boxes.</p> <p>The 9 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file.</p> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Oostpoort Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>The Oostpoort dataset is for detecting floating litter with computer vision. We generated this dataset from experiments conducted during 26 days from February to March 2022, in a canal at Oostpoort, Delft, the Netherlands. We collected data employing action cameras (GoPro MAX 360 and GoCam3) mounted outside the windows of a tower at Oostpoort with a viewing angle of 0 degree. We recorded video sequences with a time-lapse recording (1 image/30 sec) and a FPS (frame per second) of 17.98. We generated the Oostpoort dataset by saving images from these videos. The resolution of images are 3840*2160 and 1920*1440. This dataset consists of 562 RGB images. We manually labeled the litter items in these images with bounding boxes.</p> <p>The 562 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file. The <em>Oostpoort Dataset.xlsx</em> file contains the detailed information of images, including collecting date, collecting time, device, device location (in a bridge), device degree, device height, weather conditions, the number of images, and the number of annotated litter items.</p> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Groningen Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>The Groningen dataset is for detecting floating litter with computer vision. We conducted several experiments in a canal in Groningen, the Netherlands, in 2023. We captured data employing a security cameras (Obscape HQ time-lapse), mounted on a bridge at a height of 4m. We recorded images with a time-lapse recording (1 image/6 sec). The image resolution is 2592*1944. This dataset consists of 63 RGB images. We manually labeled the litter items in these images with bounding boxes.</p> <p>The 63 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file. </p> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Data from: Riparian leaf litter decomposition on pond bottom after a retention on floating vegetation
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Floating litter with epibionts Central and South America - Supplementary Data
<p>Supplementary data: Surveys of litter with interactions on beaches from Mexico to Chile. Data validated for the positive buoyancy and the presence of epibionts by type of litter. Main groups of organisms identified growing on litter. </p>
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