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7 results for “Synthetic Training Data”
Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data
<p>This dataset contains the latest version of a selection of result data of the paper "Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data" (DOI: https://doi.org/10.1016/j.rse.2025.114740 )</p> <p>The dataset contains:</p> <ol> <li>A geopackage of training points of pure tree species</li> <li>The resulting 12-band tree species fraction map of Rhineland-Palatinate</li> <li>HSV-colored map of dominant tree species. For information which tree species are represented by the different colors, refer to the Supplemental in the original paper.</li> <li>CSV-table of predicted and reference propotion of the tree species in the validation polygon (the original polygon data can not be published due to data privacy regulations) </li> </ol> <p> </p>
Training dataset for "A deep learned nanowire segmentation model using synthetic data augmentation"
<p>This image dataset contains synthetic structure images used for training the deep-learning based nanowire segmentation model presented in our work "A deep learned nanowire segmentation model using synthetic data augmentation" to be published in <em>npj Computational materials. </em>Detailed information can be found in the corresponding article.</p>
Synthetic data for R training
<p>Synthetic data for R training</p>
2D Synthetic Training Data For SyMBac
<p>Synthetic training datasets, used to train models to segment</p> <ul> <li><em>B. subtilis </em>growing in mother machine (100x oil, phase contrast)</li> <li><em>E. coli </em>growing on agar pads (100x oil, phase contrast)</li> <li><em>E. coli </em>streaked onto agar pads (60x air, fluorescence)</li> <li><em>E. coli </em>growing in a microfluidic turbidostat (100x oil, phase contrast)</li> </ul>
Data for paper "Using synthetic semiochemicals to train canines to detect bark beetle-infested trees" in Ann For Sci
<p><strong>ESM_0</strong> Photo. Entrainment of semiochemicals with Porapak <sup>® </sup>Q plug from cylinders used in stimuli delivery in dog training platform. (DOCX)</p> <p><strong>ESM_1</strong> Fig. Educational scent platform. (PDF)<br> <strong>ESM_2 </strong>Fig. Training platform stimuli layout and decline in response to no<br> target scent. (PDF)<br> <strong>ESM_3 </strong>Table. Evaluation of the dog detection performance as number of<br> indications with decreasing amounts of scent molecules over time. (PDF)</p> <p><strong>ESM_4_V1</strong> Video. Educational scent platform in operation. (AVI)<br> <strong>ESM_4_V2</strong> Video.<em> </em>Placement of cotton scent pad and the location of the scent by dog on a pine (a non-host tree of the beetle). (AVI)<br> <strong>ESM_4_V3</strong> Video. The search, GPS tracking, and location of natural attacks.<em> </em>(AVI)<br> <strong>ESM_4_V4</strong> Video. The search, location of two adjacent natural attacks, and rewarding. (AVI)</p> <p>The dog detection allows timely removal by sanitation logging of first beetle-attacked trees before offspring emergence, preventing local beetle increases. Detection dogs rapidly learned responding to synthetic bark beetle pheromone components, with known chemical titres, allowing search training during winter in laboratory and field. Dogs trained on synthetics detected naturally attacked trees in summer at a distance of >100 m.</p>
Dataset from "Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective"
<p>This dataset contains the images and ground truth label masks for semantic segmentation created and described in "Hinniger, C.; Rüter, J. Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective. Aerospace 2023, 10, 604. https://doi.org/10.3390/aerospace10070604".</p>
Synthetic and reticulated foam solid and velocity data used to train and validate CNN models
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Allen Brain Atlas
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International Brain Laboratory public data
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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.