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5 results for “Street-level”
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>
Outsourcing asylum reception Street-level organizations and the privatization of state action in Switzerland
<p>By approaching outsourcing through an ethnographic lens,we focus on the policies that enable public authorities to deal with the many uncertainties of refugee reception. We investigate how cantonal (subnational) governments in Switzerland remodel these uncertainties by implementing the policy of reception through private intermediaries, namely actors, instruments, and rationalities from the private sphere.</p> <p>Codebook along DDI standard in pdf and xml formats.</p>
Mapillary POI-Neighborhood Street-Level Images (MPOINSLI)
<p><em><strong>Dataset Name:</strong></em> MPOINSLI Mapillary POI-Neighborhood Street-Level Images </p> <blockquote> <p>This is a repository of Mapillary street-view images of New York City that include any portion of POIs in their field of view. The repository is the outcome of a paper, the abstract of which is provided below. Please use the below citatin for using this dataset:</p> <p> </p> </blockquote> <p><strong>Citation: </strong></p> <p>N. Zarbakhsh and G. McArdle, "Points-of-Interest from Mapillary Street-level Imagery: A Dataset For Neighborhood Analytics," 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), Anaheim, CA, USA, 2023, pp. 154-161, doi: 10.1109/ICDEW58674.2023.00030.</p> <p><strong>Abstract: </strong></p> <p>The Sustainable Development Goals of the United Nations promote sustainable urban development to make cities more economically and socially liveable. Points of Interest (POIs) such as commercial properties and healthcare facilities are significant markers for these goals. Street-view images are becoming increasingly important for capturing cities' streetscapes. Existing studies provide city-level images, while there are few studies that provide images in the vicinity of certain POIs. Therefore, this paper develops a framework for filtering images so that a portion of a given POI is visible in their field of view (FOV). We contribute with Mapillary POI-Neighborhood Street-Level Images (MPOINSLI) dataset, a large street-view image of POIs and their neighborhood in New York City. First, all the images within a 35-meter radius of certain POIs are filtered. Then, the intersection technique is utilized to determine if the cameras' FOV triangular polygons intersect the POIs' polygons. Using 11,126 POIs from SafeGraph's Geometry and Place datasets in conjunction with 875,592 Mapillary images, we demonstrate the effectiveness of our approach. MPOINSLI contains 167,743 Mapillary street-view images of 6,732 unique POIs, defined by the standard identifiers (Placekeys) which are further classified into 23 general functionalities categories (top-categories) and 67 more specific categories (sub-categories) of the POIs. MPOINSLI provides an open-source repository that contains metadata such as raw and post-processed camera-related parameters, the Harvesian distance between the camera and the POI's coordinates, and the intersection area. MPOINSLI could provide promising future applications for both smart cities and computer vision, including scene recognition across POI neighborhoods and fine-grained land-use classification.</p>
StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature selection
<p>The dataset consists of two .zip files.</p> <p>The first .zip file named "annotated dataset" contains a folder named “annotated dataset" with annotated street images. It consists of the folder “images” that has 763 image files. The image format is PNG. 432 images have dimensions of 1080 x 2160 and 331 have dimensions of 866 x 2400. The filenames are random uuids. The “annotated dataset” folder also contains the annotations in the file “coco_annotations.json”. Annotations are provided in COCO format. Table 1 shows the total number of annotated objects per class.</p> <table align="center" summary="Total number of annotated objects per class"> <caption><em>Table 1. Total number of annotated objects per class</em></caption> <thead> <tr> <th scope="col"><strong><em>Class</em></strong></th> <th scope="col"><strong><em>Annotated Objects</em></strong></th> </tr> </thead> <tbody> <tr> <td><em>Tree</em></td> <td>1922</td> </tr> <tr> <td><em>Waste Bin</em></td> <td><em>223</em></td> </tr> <tr> <td><em>Recycling Bin</em></td> <td>181</td> </tr> <tr> <td><em>Lighting Pole</em></td> <td><em>716</em></td> </tr> <tr> <td><em>Shop Storefront</em></td> <td><em>628</em></td> </tr> </tbody> </table> <p>The second .zip file is named "routes" and contains a folder named “routes” with consecutive frames of four different driving routes in the city of Thessaloniki and their corresponding GPS signal. So the folder “routes” contains 4 folders in the following format “VID_<YYYYMMDD>_<HHmmSS>” where Y denotes digits for year, M denotes digits for month, D denotes digits for day, H denotes digits for hour, m denotes digits for minutes and S denotes digits for seconds. Not the filename represents the start of the collection sequence. All street data was collected in 2022. Each route folder has the “images” folder which contains the consecutive street image data. Image data in this folder is in JPEG format. Each filename in ‘images’ has the frame_<id>.jpg format where id denotes the order of the frame. Table 2 shows more details regarding the number of frames and frame dimension of the driving routes.</p> <table align="center" summary="Total number of annotated objects per class"> <caption><em>Table 2. Total number frames and frame dimensions for each of the routes</em></caption> <thead> <tr> <th scope="col"><strong><em>Route Name</em></strong></th> <th scope="col"><strong><em>Frames Number</em></strong></th> <th scope="col"><strong><em>Frame Dimension</em></strong></th> <th scope="col"><strong><em>Route duration</em></strong></th> </tr> </thead> <tbody> <tr> <td><em>VID_20220617_111456</em></td> <td><em>41,650</em></td> <td><em>1080 x 2160</em></td> <td>1h, 9m, 26s</td> </tr> <tr> <td><em>VID_20220210_112926</em></td> <td><em>23.035</em></td> <td><em>866 x 2400</em></td> <td>38m, 26s</td> </tr> <tr> <td><em>VID_20220209_114831</em></td> <td><em>18.000</em></td> <td><em>1080 x 2160</em></td> <td>30m, 3s</td> </tr> <tr> <td><em>VID_20220209_123323</em></td> <td><em>18.273</em></td> <td><em>1080 x 2160</em></td> <td>30m, 30s</td> </tr> </tbody> </table> <p>Each route folder contains a “gps.json” file which contains latitude and longitude information for each frame. This file is essentially a JSON list of objects that each object contains the “frame_name” attribute and the corresponding “coordinates” object which contains the “latitude” and "longitude" attributes.</p>
Augmented Street-Level Imagery with Points of Interest (Data and Resources Track)
<p>Augmented Street-Level Imagery with Points of Interest (Data and Resources Track)</p>
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