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7 results for “road type”
StreetSurfaceVis: a dataset of street-level imagery with annotations of road surface type and quality
<h1>StreetSurfaceVis</h1> <p><em>StreetSurfaceVis</em> is an image dataset containing <strong>9,122 street-level images from Germany</strong> with labels on <strong>road surface type and quality.</strong> The CSV file <code>streetSurfaceVis_v1_0.csv</code> contains all image metadata and four folders contain the image files. All images are available in four different sizes, based on the image width, in 256px, 1024px, 2048px and the original size.<br>Folders containing the images are named according to the respective image size. Image files are named based on the <code>mapillary_image_id</code>.</p> <p>You can find the corresponding publication here: <a href="https://www.nature.com/articles/s41597-024-04295-9#citeas">StreetSurfaceVis: a dataset of crowdsourced street-level imagery with semi-automated annotations of road surface type and quality</a></p> <p> </p> <h3>Image metadata</h3> <p>Each CSV record contains information about one street-level image with the following attributes:</p> <ul> <li><code>mapillary_image_id</code>: ID provided by Mapillary (see information below on Mapillary)</li> <li><code>user_id</code>: Mapillary user ID of contributor</li> <li><code>user_name</code>: Mapillary user name of contributor</li> <li><code>captured_at</code>: timestamp, capture time of image</li> <li><code>longitude</code>, <code>latitude</code>: location the image was taken at</li> <li><code>train</code>: Suggestion to split train and test data. `True` for train data and `False` for test data. Test data contains data from 5 cities which are excluded in the training data.</li> <li><code>surface_type</code>: Surface type of the road in the focal area (the center of the lower image half) of the image. Possible values: asphalt, concrete, paving_stones, sett, unpaved</li> <li><code>surface_quality</code>: Surface quality of the road in the focal area of the image. Possible values: (1) excellent, (2) good, (3) intermediate, (4) bad, (5) very bad (see the attached <strong>Labeling Guide document</strong> for details)</li> </ul> <p> </p> <h3>Image source</h3> <p>Images are obtained from <a href="https://www.mapillary.com/">Mapillary</a>, a crowd-sourcing plattform for street-level imagery. More metadata about each image can be obtained via the <a href="https://www.mapillary.com/developer/api-documentation">Mapillary API . </a>User-generated images are shared by Mapillary under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA</a> License.</p> <p>For each image, the dataset contains the <code>mapillary_image_id</code> and <code>user_name</code>. <br>You can access user information on the Mapillary website by <code>https://www.mapillary.com/app/user/<USER_NAME> </code><br>and image information by <code>https://www.mapillary.com/app/?focus=photo&pKey=<MAPILLARY_IMAGE_ID></code></p> <p>If you use the provided images, please adhere to the <a href="https://www.mapillary.com/terms">terms of use of Mapillary.</a></p> <p> </p> <h3>Instances per class</h3> <p>Total number of images: 9,122</p> <table> <tbody> <tr> <td> </td> <td><strong>excellent</strong></td> <td><strong>good</strong></td> <td><strong>intermediate</strong></td> <td><strong>bad</strong></td> <td><strong>very bad</strong></td> </tr> <tr> <td><strong>asphalt</strong></td> <td>971</td> <td>1697</td> <td>821</td> <td>246</td> <td>-</td> </tr> <tr> <td><strong>concrete</strong></td> <td>314</td> <td>350</td> <td>250</td> <td>58</td> <td>-</td> </tr> <tr> <td><strong>paving stones</strong></td> <td>385</td> <td>1063</td> <td>519</td> <td>70</td> <td>-</td> </tr> <tr> <td><strong>sett</strong></td> <td>-</td> <td>129</td> <td>694</td> <td>540</td> <td>-</td> </tr> <tr> <td><strong>unpaved</strong></td> <td>-</td> <td>-</td> <td>326</td> <td>387</td> <td>303</td> </tr> </tbody> </table> <p> </p> <p>For modeling, we recommend using a train-test split where the test data includes geospatially distinct areas, thereby ensuring the model's ability to generalize to unseen regions is tested. We propose five cities varying in population size and from different regions in Germany for testing - images are tagged accordingly.</p> <p>Number of test images (train-test split): 776</p> <h3>Inter-rater-reliablility</h3> <p>Three annotators labeled the dataset, such that each image was annotated by one person. Annotators were encouraged to consult each other for a second opinion when uncertain.<br>1,800 images were annotated by all three annotators, resulting in a <em>Krippendorff's alpha</em> of 0.96 for surface type and 0.74 for surface quality.</p> <h3>Recommended image preprocessing</h3> <p>As the focal road located in the bottom center of the street-level image is labeled, it is recommended to crop images to their lower and middle half prior using for classification tasks.</p> <p>This is an exemplary code for recommended image preprocessing in <strong>Python</strong>:</p> <pre><code>from PIL import Image<br></code><code>img = Image.open(image_path)</code><br><code>width, height = img.size</code><br><code>img_cropped = img.crop((0.25 * width, 0.5 * height, 0.75 * width, height))</code></pre> <h3><br><strong>License</strong></h3> <p><a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA</a></p> <p> </p> <h3><strong>Citation</strong></h3> <p>If you use this dataset, please cite as: </p> <p> </p> <p>Kapp, A., Hoffmann, E., Weigmann, E. <em>et al.</em> StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality. <em>Sci Data</em> <strong>12</strong>, 92 (2025). https://doi.org/10.1038/s41597-024-04295-9</p> <p> </p> <p><code>@article{kapp_streetsurfacevis_2025,<br> title = {{StreetSurfaceVis}: a dataset of crowdsourced street-level imagery annotated by road surface type and quality},<br> volume = {12},<br> issn = {2052-4463},<br> url = {https://doi.org/10.1038/s41597-024-04295-9},<br> doi = {10.1038/s41597-024-04295-9},<br> pages = {92},<br> number = {1},<br> journaltitle = {Scientific Data},<br> shortjournal = {Scientific Data},<br> author = {Kapp, Alexandra and Hoffmann, Edith and Weigmann, Esther and Mihaljević, Helena},<br> date = {2025-01-16},<br>}</code></p> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>This is part of the SurfaceAI project at the University of Applied Sciences, HTW Berlin.</p> <p><br>- Prof. Dr. Helena Mihajlević<br>- Alexandra Kapp<br>- Edith Hoffmann<br>- Esther Weigmann</p> <p>Contact: surface-ai@htw-berlin.de</p> <p>https://surfaceai.github.io/surfaceai/</p> <p><strong>Funding</strong>: SurfaceAI is a mFund project funded by the Federal Ministry for Digital and Transportation Germany.</p> <p> </p>
RoadArrowORIEN: dataset of 6701 images (64x64 pixels) of straight arrow-type road markings and their azimuths
<p>The dataset consisting of 6701 PNG images (64x64 pixels) of straight arrow type road markings with different dimensions and orientations, together with the angle it forms with respect to the vertical axis (azimuth), has been constructed in the framework of the SROADEX project to train a regression neural network to calculate the azimuth of other straight arrows that can be identified in high resolution aerial orthoimages.</p> <p>The dataset was created by labeling with the labelme tool the straight arrows on orthoimage tesserae of 256x256 pixels. After semantic labeling by drawing an arrow on the orthoimage, the data was automatically processed to calculate the azimuthal angle. The procedure followed is as follows:<br> 1.- The vertices of the generated arrow-shaped polygon have been extracted.<br> 2.- Clusters of nearby points have been generated, with a minimum of 2 points and the cluster with less points and the one with more points has been identified. <br> 3.- For the clusters, the centroid has been generated, preserving the information of the number of vertices that define the cluster.<br> 4.- Then a vector has been generated with origin in the centroid of the arrow with less vertices grouped and as end the centroid with the highest number of vertices.<br> 5.- Finally, the azimuth of this vector with respect to the ordinate axis has been calculated, <br> The images have been automatically cropped with a constant size of 64x64 pixels taking an extension greater than the occupied of the arrow in the scene.</p> <p>The shared dataset consists of a compressed file with the images and a text file in CSV format with the names of the images and the normaliced [0..1] Azimutal angles of the straight arrows contained in the images.</p>
Roads: Location and Type - Ipswich Watershed - Idrisi Raster File.
This data layer contains the roads within the Ipswich River Watershed, according to MassGIS (http://www.state.ma.us/mgis/) as of July 17, 2002. THis datalayer is part of a group of layers used for research in the Ipswich River Watershed. To show the location and type of roads within the watershed.
Text-fig. 1. a: Simplified geological map of the Permo-Carboniferous Brive Basin (after Feys 1989) with marked localities L 1 (Brive, road D1089), L 2 (Lanteuil). b: Profile of the Brive Basin (modified from Feys 1989). c: Profile recorded by Guy and Maryse Chantepie in 2007 on the type locality Brive, road D1089. in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central
Text-fig. 1. a: Simplified geological map of the Permo-Carboniferous Brive Basin (after Feys 1989) with marked localities L 1 (Brive, road D1089), L 2 (Lanteuil). b: Profile of the Brive Basin (modified from Feys 1989). c: Profile recorded by Guy and Maryse Chantepie in 2007 on the type locality Brive, road D1089.
Data from: Road disturbance shifts root fungal symbiont types and reduces the connectivity of plant-fungal co-occurrence networks in mountains
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urbisphere_gb-london_UR-7: Gridded total road lengths by type for London, UK
<h2>Files in this Archive </h2> <ul> <li>London_road_lengths_by_type.zip <ul> <li>Polygons, ESRI shapefile format (*.shp, *.shx, *.cpg, *.dbf, *.prj)</li> <li>Total road lengths by road type (all lanes and traffic flow directions) in 500-m grid-boxes covering Greater London, UK</li> </ul> </li> <li>code.zip <ul> <li>Python3</li> <li>Process input road data in 500-m grid-boxes covering Greater London, UK </li> </ul> </li> <li>urbisphere_gb-london_UR-7.pdf <ul> <li>Documentation</li> </ul> </li> </ul> <h2>Data purpose </h2> <p>The data support urbisphere–London modelling activities, including the generation of a travel route database for private vehicle transport on roads.</p> <h3>Linked with</h3> <ul> <li>McGrory et al. 2024. urbisphere_gb-london_UR-3: Transport database derived from UK road networks, OSM data, and TfL public transport timetables, for modelling in London, UK. urbisphere–London Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889780</li> <li>Hertwig et al. 2024. urbisphere_gb-london_UR-1: Processing and modelling grid of 500-m horizontal resolution for London, UK. urbisphere–London Data Release and Technical Documentation [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10889756</li> </ul>
Glycemic Response to Road Cycling in Type 1 Diabetes
ClinicalTrials.gov study NCT01398826. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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