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51 results for “Water segmentation”
Water Segmentation Dataset
<p>The dataset is composed of 490 images and their labelled ground truth, which consists of binary masks where zero is assigned to the background pixels and one to the water pixels. You can get the full dataset of 11900 images with their mask at the following link: <a href="https://www.google.com/url?q=https://drive.google.com/file/d/1Tm0p7XLzpLlXycSxxu2X7WENTYHh97qC/view?usp%3Dsharing&source=gmail-imap&ust=1652790604000000&usg=AOvVaw2QczvwwMAXYdQwaSuoDeXw">https://drive.google.com/file/d/1Tm0p7XLzpLlXycSxxu2X7WENTYHh97qC/view?usp=sharing</a></p>
FIGURE 9. Spionid adult morphology. Posterior segments and pygidia. A in Spionidae (Annelida) from shallow waters around the British Islands: an identification guide for the NMBAQC Scheme with an overview of spionid morphology and biology
FIGURE 9. Spionid adult morphology. Posterior segments and pygidia. A, Prionospio sp., left lateral view, pygidium with long middorsal cirrus and two shorter ventral cirri. B, Pygospio elegans, dorsal view, pygidium with one pair of dorsal cirri and one pair of ventral cirri. C, Malacoceros sp., rear view, pygidium with five dorsal cirri and one pair of ventral cirri. D, Dipolydora cardalia, rear view, pygidium with two dorsal lobes and one large ventral lobe. E, Polydora cornuta, left lateral view, funnel-shaped pygidium with dorsal gap. F, Scolelepis sp., left lateral view, cushion-like pygidium. an—anus; dl—dorsal lobe; lo—lateral ciliated organ; mc—middorsal cirrus; vc—ventral cirri; vl—ventral lobe. Scales: A, B, F, E—30 µm. C—50 µm. D—100 µm.
FIGURE 3. Spionid adult morphology. Head and anterior segments. A in Spionidae (Annelida) from shallow waters around the British Islands: an identification guide for the NMBAQC Scheme with an overview of spionid morphology and biology
FIGURE 3. Spionid adult morphology. Head and anterior segments. A, Dispio sp., anterior end, left lateral view, pointed prostomium, long notochaetae of segment 1 directed anteriorly, and papillated postchaetal lamellae on anterior segments, palps missing. B, Aonides sp., anterior end, left lateral view, conical prostomium and branchiae from segment 2, palps missing. C, Scolelepis sp., anterior end, left lateral view, pointed snout composed by anterior peristomial part and posterior prostomial part, and coiled palps with transverse ridges bearing short non-motile cilia. D, Streblospio benedicti, anterior end, left lateral view, peristomial hood, short palps, one pair of branchiae on chaetiger 1, and lateral ciliated organ on achaetous and apodous segment 1. E, Paraprionospio sp., anterior end, dorsal view, peristomial hood enveloping prostomium, palps and first two pairs of branchiae missing. F, Paraprionospio sp., anterior end, left lateral view, peristomial hood, basal sheath on palp, three pairs of branchiae, and lateral ciliated organ on achaetous and apodous segment 1. br—branchia; bs—basal sheath enveloping basal part of palp; dr—dorsal ridge; lo—lateral ciliated organ; nc—notopodial capillaries; pa—palp; pe—peristomium; ph— peristomial hood; pr—prostomium. Scales: A, E—200 µm. B, D—50 µm. C, F—100 µm.
An adaptive segment anything model for water level measurement with staff gauge images: staff gauge images and in-situ water levels
<p><span>We installed a commercial time-lapse camera on an urban lake in South China to collect the staff gauge images with a interval of 15 minutes. The data samples spanned from May 27th, 0:00 am, to July 6th, 11:00 am, 2023 (UTC+8), including a total of 2691 images. All the collected images were manually read to derive the stages as ground truth. The image and water level data is stored in this dataset.</span></p> <p> </p>
Coastal Satellite Image Segmentation (Water and Land) Labels: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)
<p>Contained here are jpegs containing coastal RGB satellite images along with a water vs. land mask. Each image is 256 pixels by 256 pixels. </p> <p>Geographic scope: Delmarva (USA), Virginia Beach (USA), New Jersey (USA), Long Island (USA), Duck, NC (USA), Northern Tuscany Littoral Cell (Italy), Torrey Pines, CA, (USA), Narrrabeen Beach (Australia), Truc Vert (France)</p> <p>Temporal range: 1984 to 2022</p> <p>Satellites: Landsat 5, 7, 8 and Sentinel-2</p> <p>All images were downloaded from Google Earth Engine using CoastSat download tools.</p> <p>The datasets are arranged into 'train', 'val', and 'test' folders. Within each of those folders are two folders 'a' and 'b'. 'a' contains the images (RGB), whereas 'b' contains the labels (land vs. water mask).</p> <p>All images were augmented with the four following augmentations: horizontal flip, vertical flip, 90 degree clockwise rotation, 90 degree counterclockwise rotation, and a horizontal+vertical flip. </p> <p>For training a new segmentation model, it is advised to not do any of these rotational or flip augmentations since they have already been performed. Instead, possibly experiment with other augmentations like introducing noise into the imagery.</p> <p>These images were used to train an image-to-image translation generative adversarial network. The code and model weights (generator and discriminator) are available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>To get to the files locally, you can download the .zip from Zenodo and then unzip the .zip file.</p>
FIGURE 8. Abdominal segments IX and X in Morphology and feeding biology of larvae of the South African endemic water beetle genus Prosthetops Waterhouse, 1879 (Coleoptera: Hydraenidae)
FIGURE 8. Abdominal segments IX and X of third instar larva of Prosthetops wolfbergensis. a: Dorsal view; b: Ventral view; c: Lateral view. Scale bar: 0.3 mm.
Dataset For Water Segmentation
<p>The dataset is composed of 1011 images and their labeled ground truth, used for water segmentation in the “Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera” paper.</p> <p>Images were obtained from the European Flood 2013 Dataset and some were manually saved from the Selangor Infobanjir website found at http://infobanjirjps.selangor.gov.my/camera.html. The labeling task was done in Matlab software (R2020b)</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)</p> <p><strong>Description</strong></p> <p>3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only</p> <p>These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water</li> </ul> <p><strong>File descriptions</strong></p> <ul> <li> classes.txt, a file containing the class names</li> <li> images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li> labels.zip, a zipped folder containing the 1-band label images</li> <li> nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li> swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li> overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li> resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li> resized_labels.zip, label images resized to 512x512x1 pixels</li> <li> resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li> resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> <p>References</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.</p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p> <p>***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information</p> <p>****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p>
Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p>Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</p> <p>These Residual-UNet models have been created using Segmentation Gym*</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p>classes:</p> <ol> <li>water</li> <li>other</li> </ol> <p><br> <strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym</p>
Expression data from root segments of barley (Hordeum vulgare) during a transient water deficit repressing lateral root formation
GEO Series GSE67367. Hordeum vulgare. 14 samples. Type: Expression profiling by array.
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International Brain Laboratory public data
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OpenNeuro
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