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FIGURE 17. Centruroides Marx, 1890, metasomal segments I and II, dorsal aspect. A, D. C. rileyi Sissom, 1995, A in Systematic Revision Of The Arboreal Neotropical "Thorellii" Clade Of Centruroides Marx, 1890, Bark Scorpions (Buthidae C.L. Koch, 1837) With Descriptions Of Six New Species
FIGURE 17. Centruroides Marx, 1890, metasomal segments I and II, dorsal aspect. A, D. C. rileyi Sissom, 1995, A. ♂ (CNAN SC4002), D. ♀ (CNAN SC4003). B, E. C. hamadryas, sp. nov., B. holotype ♂ (CNAN T01408), E. paratype ♀ (CNAN T01415). C, F. C. hoffmanni Armas, 1996, C. ♂, F. ♀ (CNAN SC3996). G, J. C. cuauhmapan, sp. nov., G. holotype ♂ (CNAN T01396), J. paratype ♀ (CNAN T01399). H, K. C. berstoni, sp. nov., H. holotype ♂ (CASENT 9073325), K. paratype ♀ (CASENT 9073313). I, L. C. chanae, sp. nov., I. holotype ♂ (CNAN T01403), L. paratype ♀ (CNAN T01405). M, P. C. catemacoensis, sp. nov., M. holotype ♂ (CNAN T01424), P.paratype ♀ (CNAN T01423). N, Q. C. schmidti Sissom, 1995, N. ♂ (CASENT 9073316), Q. ♀ (CASENT 9073317). O, R. C. yucatanensis, sp. nov., O. holotype ♂ (CNAN T01416), R. paratype ♀ (CNAN T01417. Scale bars = 2 mm.
FIGURE 19. Centruroides Marx, 1890, metasomal segments I and II, lateral aspect. A, D. C. rileyi Sissom, 1995, A in Systematic Revision Of The Arboreal Neotropical "Thorellii" Clade Of Centruroides Marx, 1890, Bark Scorpions (Buthidae C.L. Koch, 1837) With Descriptions Of Six New Species
FIGURE 19. Centruroides Marx, 1890, metasomal segments I and II, lateral aspect. A, D. C. rileyi Sissom, 1995, A. ♂ (CNAN SC4002), D. ♀ (CNAN SC4003). B, E. C. hamadryas, sp. nov., B. holotype ♂ (CNAN T01408), E. paratype ♀ (CNAN T01415). C, F. C. hoffmanni Armas, 1996, C. ♂, F. ♀ (CNAN SC3996). G, J. C. cuauhmapan, sp. nov., G. holotype ♂ (CNAN T01396), J. paratype ♀ (CNAN T01399). H, K. C. berstoni, sp. nov., H. holotype ♂ (CASENT 9073325), K. paratype ♀ (CASENT 9073313). I, L. C. chanae, sp. nov., I. holotype ♂ (CNAN T01403), L. paratype ♀ (CNAN T01405). M, P. C. catemacoensis, sp. nov., M. holotype ♂ (CNAN T01424), P. paratype ♀ (CNAN T01423). N, Q. C. schmidti Sissom, 1995, N. ♂ (CASENT 9073316), Q. ♀ (CASENT 9073317). O, R. C. yucatanensis, sp. nov., O. holotype ♂ (CNAN T01416), R. paratype ♀ (CNAN T01417). Scale bars = 2 mm.
FIGURE 18. Centruroides Marx, 1890, metasomal segments I and II, ventral aspect. A, D. C. rileyi Sissom, 1995, A in Systematic Revision Of The Arboreal Neotropical "Thorellii" Clade Of Centruroides Marx, 1890, Bark Scorpions (Buthidae C.L. Koch, 1837) With Descriptions Of Six New Species
FIGURE 18. Centruroides Marx, 1890, metasomal segments I and II, ventral aspect. A, D. C. rileyi Sissom, 1995, A. ♂ (CNAN SC4002), D. ♀ (CNAN SC4003). B, E. C. hamadryas, sp. nov., B. holotype ♂ (CNAN T01408), E. paratype ♀ (CNAN T01415). C, F. C. hoffmanni Armas, 1996, C. ♂, F. ♀ (CNAN SC3996). G, J. C. cuauhmapan, sp. nov., G. holotype ♂ (CNAN T01396), J. paratype ♀ (CNAN T01399). H, K. C. berstoni, sp. nov., H. holotype ♂ (CASENT 9073325), K. paratype ♀ (CASENT 9073313). I, L. C. chanae, sp. nov., I. holotype ♂ (CNAN T01403), L. paratype ♀ (CNAN T01405). M, P. C. catemacoensis, sp. nov., M. holotype ♂ (CNAN T01424), P.paratype ♀ (CNAN T01423). N, Q. C. schmidti Sissom, 1995, N. ♂ (CASENT 9073316), Q. ♀ (CASENT 9073317). O, R. C. yucatanensis, sp. nov., O. holotype ♂ (CNAN T01416), R. paratype ♀ (CNAN T01417. Scale bars = 2 mm.
FIGURE 21. Centruroides Marx, 1890, metasomal segment V, ventral aspect. A, D. C. rileyi Sissom, 1995, A in Systematic Revision Of The Arboreal Neotropical "Thorellii" Clade Of Centruroides Marx, 1890, Bark Scorpions (Buthidae C.L. Koch, 1837) With Descriptions Of Six New Species
FIGURE 21. Centruroides Marx, 1890, metasomal segment V, ventral aspect. A, D. C. rileyi Sissom, 1995, A. ♂ (CNAN SC4002), D. ♀ (CNAN SC4003). B, E. C. hamadryas, sp. nov., B. holotype ♂ (CNAN T01408), E. paratype ♀ (CNAN T01415). C, F. C. hoffmanni Armas, 1996, C. ♂, F. ♀ (CNAN SC3996). G, J. C. cuauhmapan, sp. nov., G. holotype ♂ (CNAN T01396), J. paratype ♀ (CNAN T01399). H, K. C berstoni, sp. nov., H. holotype ♂ (CASENT 9073325), K. paratype ♀ (CASENT 9073313). I, L. C. chanae, sp. nov., I. holotype ♂ (CNAN T01403), L. paratype ♀ (CNAN T01405). M, P. C. catemacoensis, sp. nov., M. holotype ♂ (CNAN T01424), P. paratype ♀ (CNAN T01423). N, Q. C. schmidti Sissom, 1995, N. ♂ (CASENT 9073316), Q. ♀ (CASENT 9073317). O, R. C. yucatanensis, sp. nov., O. holotype ♂ (CNAN T01416), R. paratype ♀ (CNAN T01417). Scale bars = 2 mm.
FIGURE 20. Centruroides Marx, 1890, metasomal segment V, dorsal aspect. A, D. C. rileyi Sissom, 1995, A in Systematic Revision Of The Arboreal Neotropical "Thorellii" Clade Of Centruroides Marx, 1890, Bark Scorpions (Buthidae C.L. Koch, 1837) With Descriptions Of Six New Species
FIGURE 20. Centruroides Marx, 1890, metasomal segment V, dorsal aspect. A, D. C. rileyi Sissom, 1995, A. ♂ (CNAN SC4002), D. ♀ (CNAN SC4003). B, E. C. hamadryas, sp. nov., B. holotype ♂ (CNAN T01408), E. paratype ♀ (CNAN T01415). C, F. C. hoffmanni Armas, 1996, C. ♂, F. ♀ (CNAN SC3996). G, J. C. cuauhmapan, sp. nov., G. holotype ♂ (CNAN T01396), J. paratype ♀ (CNAN T01399). H, K. C. berstoni, sp. nov., H. holotype ♂ (CASENT 9073325), K. paratype ♀ (CASENT 9073313). I, L. C. chanae, sp. nov., I. holotype ♂ (CNAN T01403), L. paratype ♀ (CNAN T01405). M, P. C. catemacoensis, sp. nov., M. holotype ♂ (CNAN T01424), P. paratype ♀ (CNAN T01423). N, Q. C. schmidti Sissom, 1995, N. ♂ (CASENT 9073316), Q. ♀ (CASENT 9073317). O, R. C. yucatanensis, sp. nov., O. holotype ♂ (CNAN T01416), R. paratype ♀ (CNAN T01417). Scale bars = 2 mm.
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
<p>There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. ClevrTex has 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings.</p> <p>Project webpage: https://www.robots.ox.ac.uk/~vgg/data/clevrtex/</p> <p>These are <strong>the variant datasets</strong>. Please see project page for links to the main dataset.</p>
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
<p>There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. ClevrTex has 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings.</p> <p>Project webpage: https://www.robots.ox.ac.uk/~vgg/data/clevrtex/</p> <p>This is the <strong>main dataset and OOD test set. </strong>Please see project page for links to the dataset variants.</p>
FIGURE 27. Rumikiru, n. gen., metasomal segment V, ventral aspect. A, C. Rumikiru atacama, n in Rumikiru, n. gen. (Scorpiones: Bothriuridae), a New Scorpion Genus from the Atacama Desert
FIGURE 27. Rumikiru, n. gen., metasomal segment V, ventral aspect. A, C. Rumikiru atacama, n. sp. B, D. Rumikiru lourencoi (Ojanguren-Affilastro, 2003), n. comb. A, B. ♂ (MACN-Ar). C, D. ♀ (MACN-Ar). Scale bars = 1 mm.
Fig. 1. Allokoenenia afra Silvestri, 1913. A. Opisthosomal segments VIII–XI in Two extraordinary troglobitic species of Allokoenenia (Eukoeneniidae: Palpigradi) from Brazil: first records of this initially monotypic genus more than a century after its description
Fig. 1. Allokoenenia afra Silvestri, 1913. A. Opisthosomal segments VIII–XI and first flagellar segment, ♀, typus. B. Flagellum (second to fourteenth flagellar segments), ♀, typus. C. Frontal organ, immature, cotypus. Scale bars: A–B = 100 μm; C = 10 μm.
Cell Instance Segmentation Dataset
<p>This project contains the data for the paper:</p> <p>A. Bouyssoux, R. Fezzani and J. -C. Olivo-Marin, "<em>Cell Instance Segmentation Using Z-Stacks In Digital Cytopathology,</em>" 2022 IEEE International Symposium on Biomedical Imaging (ISBI), 2022.</p> <p>The code associated with this project is available at: <a href="https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation">https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation</a></p> <p>A new large Cell Instance Segmentation Dataset (CISD) is introduced. It comprises 3911 samples containing at least two touching or overlapping urothelial cells. Cell instances were manually annotated by trained cytotechnicians. All samples are extracted from 30 digital cytology slides stained with nine variations of Papanicolaou staining. The cytology slides are prepared from urine samples from healthy patients, using a Hologic ThinPrep®5000 processor, and routinely stained with the Agilent Dako CoverStainer®. The slides are finally digitized using a Hamamatsu NanoZoomer®S360 with 21 focal planes and centered on the best focus plane determined by the scanner autofocus.</p> <p>Note that cell instances with a bounding box width/height smaller than 10% of the sample width/height, as well as red blood cells and neutrophil cells were automatically filtered out because under-represented in the available data.</p> <p>Each sample is considered in three different manners in the CISD, allowing experimentation with different methods<br> for handling Z-stack data and comparison with simple 2D acquisition:</p> <ul> <li>Center slice: the best focus plane only as determined by the scanner, which is equivalent to a 2D slide acquisition.</li> <li>Extended Depth of Field (EDF): the 21 planes merged in an image where all textured parts appear in focus.</li> <li>Raw Z-stack: the volume composed of the 21 focal planes.</li> </ul> <p>Once extracted, the dataset folder contains three subfolders, one for each of the three types of samples described here above, containing the images and stack of images. A JSON file contains the instance masks, encoded in RLE format.</p>
Pretrained network for segmentation of kidneys and exophytic cysts in subjects with autosomal dominant polycystic kidney disease (ADPKD)
<p>This pretrained network based on the 3D U-Net is for segmentation of kidneys and exophytic cysts in subjects with autosomal dominant polycystic kidney disease (ADPKD). The network was trained with 157 (including 53 cases with exophytic cysts) subjects with ADPKD. The details of trained dataset and the performance of the network will be updated after our manuscript is accepted for publication.</p>
Diffraction images of a crystal of the F-BAR domain of PSTPIP1 (Proline-serine-threonine phosphatase-interacting protein 1) bound to the C-terminal homology (CTH) segment of the phosphatase LYP (PTPN22) (PDB entry 7AAM)
<p>Diffraction images of a crystal of the F-BAR domain of human PSTPIP1 (residues 1-289, Uniprot reference O43586-1) in complex with the CTH of LYP (residues 787-807, Uniprot Q9Y2R2-1).</p> <p>Data were collected on a single crystal at the beamline i03 of the Diamond Light Source synchrotron (Didcot, UK) using radiation of 0.99987 Å wavelength and a PILATUS3 6M detector. The dataset consists of 3 groups, each containing of 1800 images (0.1 degree oscillation per image), collected at three different positions of the same crystal. Crystal belongs to the space group P2(1)2(1)2(1) with unit cell dimensions a=48.0 Å, b=72.0 Å, c=205.0 Å. The asymmetric unit contains an homodimer of the F-BAR domain bound to a LYP-CTH (~53% solvent content), which is the biological complex.</p> <p>Diffraction data was notably anisotropic. The lowest resolution limit was 4.05 Å in the direction b* and the highest limits were 2.11 Å and 2.10 in the directions a* and c*, respectively.</p> <p> </p> <p>The structure derived form these data is published in:</p> <p>Manso, J.A., Marcos, T., Ruiz-Martín, V. Casas J, Alcón P, Sánchez Crespo M, Bayón Y, de Pereda JM, Alonso A <em>PSTPIP1-LYP phosphatase interaction: structural basis and implications for autoinflammatory disorders</em>. <strong>Cell. Mol. Life Sci</strong>. 79, 131 (2022). <a href="https://doi.org/10.1007/s00018-022-04173-w">https://doi.org/10.1007/s00018-022-04173-w</a></p> <p>The structure is available at the PDB under the code <strong>7AAM</strong>:</p> <p><a href="https://www.ebi.ac.uk/pdbe/entry/pdb/7aam">https://www.ebi.ac.uk/pdbe/entry/pdb/7aam</a></p>
Xenopus tissue data for testing segmentation models
<pre>This dataset is of xenopus tissue imaged with the following settings and it comes with a trained UNET model for performing the segmentation of such tissues. In order to use the segmentation model please install the vollseg-napari plugin from the napari hub and the model will be automatically downloaded for usage. Dataset was acquired by Mari Tolonen and Jakub Sedzinski, (0000-0002-4395-9022,0000-0002-1788-0329) at the university of Copenhagen and the model was trained by Varun Kapoor at Kapoorlabs. A Z projection of 21 Z slices acquired by the ImageJ Z Projection plugin was performed on the original acquired data. ObjectiveSettings ID="Objective:0" Medium="Water" RefractiveIndex="1.333"</pre> <pre>LensNA="1.2000000000000002" Model="C-Apochromat 40x/1.2 W AutoCorr M27" NominalMagnification="40.0"</pre> <pre>Physical Size X="0.6918881841365326" Physical Size X Unit="µm" </pre> <pre>Physical Size Y="0.6918881841365326" Physical Size Y Unit="µm" </pre> <pre>Physical Size Z="2.0" Physical Size Z Unit="µm"</pre> <pre>Time interval frames 1-160: 182 sec Time interval frames 161-262: 283 sec</pre> <pre>SignificantBits="8" Type="uint8"></pre> <pre>Channel AcquisitionMode="LaserScanningConfocalMicroscopy" ExcitationWavelength="488.0" ExcitationWavelengthUnit="nm" Fluor="EGFP"</pre>
DoPose: dataset for object segmentation and 6D pose estimation
<p>DoPose (Dortmund Pose)is a dataset of highly cluttered and closely stacked objects. The dataset is saved in the <a href="https://github.com/thodan/bop_toolkit/blob/master/docs/bop_datasets_format.md">BOP format</a>. The dataset includes RGB images, Depth images, 6D Pose of objects, segmentation mask (all and visible), COCO Json annotation, camera transformations, and 3D model of all objects. The dataset contains 2 different types of scenes (table and bin). Each scene contains different view angles. For the bin scenes, the data contains 183 scenes with 2150 image views. In those 183 scenes 35 scenes contain 2 views, 20 contains 3 views and 128 contains 16 views. And for table scenes, the data contains 118 scenes with 1175 image views. in Those 118 scenes, 20 scenes contain 3 views, 50 scenes with 6 images, and 48 scenes with 17 images. So in total, our data contains 301 scenes and 3325 view images. Most of the scenes contain mixed objects. The dataset contains 19 objects in total.</p> <p>For more info about the dataset content and collection process please refer to our <a href="https://arxiv.org/abs/2204.13613">Arxiv preprint</a></p> <p>If you have any questions about the dataset, please contact <strong>anas.gouda@tu-dortmund.de</strong></p>
A novel strategy for fully automated segmentation of supratentorial meningiomas: Use of pre-trained models and inclusion of normal brain images
<p>This repository is accompanying MRI datasets under the journal, titled: <strong>A novel strategy for fully automated segmentation of supratentorial meningiomas: Use of pre-trained models and inclusion of normal brain images</strong>. </p> <p>Nii_data.tar.gz (zipped) file includes MRI images of all patients described in the paper that are formatted as .nii.</p>
DeepBacs – Escherichia coli release from stationary phase - Bright field segmentation dataset and StarDist model
<p>Training and test images of live <em>E. coli</em> cells imaged under bright field for the task of segmentation.</p> <p>Additional information can be found on this<a href="https://github.com/HenriquesLab/DeepBacs/wiki"> github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli </em>cells of an overnight culture and the manually annotated segmentation mask.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field and segmented mask images </p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 2 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 px² (106 nm / pixel), 19/15 individual frames (training/test dataset)</p> <p>512 x 512 px² (106 nm / pixel), 7 regions of interest with 20 frames @ 2 min time interval (live-cell time series)</p> <p><strong>Data annotation</strong>: Images were annotated using the Fiji freehand selection tool.</p> <p><strong>Image preprocessing</strong>: Time series were stabilized using the Fiji plugin StackReg and the 480 x 480 px center region was cropped</p> <p><strong>StarDist model</strong></p> <p>The StarDist 2D model was trained from scratch for 200 epochs on 33 paired image patches (image dimensions: (512, 512 px²), patch size: (512 x 512 px²)) with a batch size of 2, 80 rays, grid size 1, 4-fold data augmentation and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.13) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU.</p> <p>Model weights can be used with the ZeroCostDL4Mic StarDist 2D notebook or the Fiji StarDist plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578 </p>
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
<p>Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain.</p> <p>Our project page can be found at <a href="http://ai.bu.edu/zerowaste/">http://ai.bu.edu/zerowaste/</a></p> <p>Please use the following password to extract the zip files: UP#1VuX409z4</p>
Data and code for: Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data
<p>UAV images used for SfM model generation and all images (both SI and OM), in which we measured grain sizes. The code used for image processing and uncertainty estimation of grain size distributions as python files and executable jupyter notebooks, where the latter also serve as documentation.</p>
Supplementary Material of : Large-Scale 3D Image Segmentation Using Scattering Networks
<p>The reader will find here the supplementary material associated with the manuscript "Large-Scale 3D Image Segmentation Using<br> Scattering Networks" submitted to IEEE Transaction of Pattern Analysis and Machine Intelligence (TPAMI), 2022.</p>
SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information
<p>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular).</p> <p>The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation.</p> <p>The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type.</p> <p>1-Road-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>1-Road-Mask</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>2-NoRoad-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p> </p>
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.