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70 results for “Semantic Segmentation”
SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline
<p>SEM images raw data X1, X1, Y1, Y2, Z1 and Z2</p>
Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images
<p>Data for the Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images paper</p>
Land Cover Aerial Imagery (LICAID) dataset for semantic segmentation
<p><strong>Dataset Highlights:</strong></p> <ul> <li><strong>Title:</strong> Land Cover Aerial Imagery Dataset (LICAID)</li> <li><strong>Focus Area:</strong> Franciacorta wine-growing region, Lombardy, Italy</li> <li><strong>Data Source:</strong> Satellite imagery from Google Earth Pro</li> <li><strong>Classes and Descriptions:</strong> <ol> <li><strong>Grasslands:</strong> Habitats dominated by grasses, with few or no trees, found in various climates from tropical to temperate regions.</li> <li><strong>Arable Land:</strong> Land predominantly used for growing crops.</li> <li><strong>Herb-dominated Habitats:</strong> Areas where non-woody plants (herbs) are the dominant vegetation, including meadows, prairies, marshes, and wetlands.</li> <li><strong>Hedgerows:</strong> Linear strips of vegetation consisting of shrubs, small trees, and grasses, often used to mark boundaries or provide wildlife habitat in agricultural landscapes.</li> <li><strong>Vineyards:</strong> Agricultural landscapes cultivated specifically for growing grapevines, typically for wine production.</li> <li><strong>Tree-dominated Man-made Habitats:</strong> Human-modified landscapes where trees are the predominant vegetation, such as urban parks, orchards, and landscaped gardens.</li> <li><strong>Olea europaea Groves:</strong> Groves or orchards of olive trees, primarily cultivated for the production of olives and olive oil, commonly found in Mediterranean regions.</li> </ol> </li> </ul> <p><strong>gy:</strong></p> <ol> <li> <p><strong>Data Acquisition:</strong></p> <ul> <li>18 orthophoto tiles manually selected from Franciacorta.</li> <li>Satellite imagery and corresponding shape files acquired from Google Earth Pro.</li> <li>Georeferencing of imagery using ArcGIS software.</li> </ul> </li> <li> <p><strong>Data Preparation:</strong></p> <ul> <li>Segmentation using multiresolution segmentation in eCognition software.</li> <li>Validation of segmented images by a plant expert using QGIS software.</li> <li>Manual annotation of seven land cover classes.</li> </ul> </li> </ol>
Video semantic segmentation with low latency
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SS-GoldenDOT: Semantic Segmentation for Mold Development
<p>A totally semantic segmentated dataset with 144 high resolution videos of 20 frames (summing a total of 2,880 images) showing the degradation due to the effects of fungi of halved Golden Delicious apples over 10 days.</p>
M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
Small spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained U-net network for detection of small random spots of 2 colors on a black background.</p><p>The network can be opened by loading "2D_SmallSpots_3cl_Unet.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
Large Spots DeepMIB project, synthetic dataset for testing 2.5D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of 2.5D semantic segmentation approaches.<br>The dataset includes trained</p><ul><li>2.5D DeepLabV3-Resnet18 depth2color (Spots_25D_DLv3RN18_Z2C_xy200z5)</li><li>2.5D U-net depth2color (Spots_25D_Unet_Z2C_xy200z5)</li></ul><p>networkd for detection of large white 3D spots on a black background. The spots that are present on a single slice only are considered as background.</p><p>The network can be opened by loading the config files "*.mibCfg" by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi/">https://mib.helsinki.fi</a></p>
A semantic segmentation dataset consisting of soil-root images with multiple growth stages.
<p> A semantic segmentation dataset consisting of soil-root images with multiple growth stages.</p> <blockquote> <p><strong>Last Update: 2022.08.10</strong></p> </blockquote>
SPARK 2024: Datasets for Spacecraft Semantic Segmentation and Spacecraft Trajectory Estimation
<p>SPARK 2024 dataset is part of SPARK challenge aims to design data-driven approaches for Spacecraft Semantic Segmentation and Trajectory Estimation. SPARK 2024 dataset will utilize data synthetically simulated with a state-of-the-art rendering engine in addition to the data collected from the Zero-Gravity Laboratory (Zero-G Lab) facility at SnT – Interdisciplinary Center for Security, Reliability, and Trust, University of Luxembourg.</p> <p>Stream 1 – Spacecraft Semantic Segmentation intended for segmenting space objects to classify pixel values based on which part of the object they belong to.</p> <p>Stream 2 – Spacecraft Trajectory Estimation intended to leveraging the knowledge of temporal data to estimate the 6DoF pose of the spacecraft.</p> <h4>Request access</h4> <p>If you would like to request access to the dataset, Please visit <a href="https://cvi2.uni.lu/spark2024/">https://cvi2.uni.lu/spark2024/</a> to register and get access to dataset.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.