StreetSurfaceVis StreetSurfaceVis is an image dataset containing 9,122 street-level images from Germany with labels on road surface type and quality. The CSV file streetSurfaceVis_v1_0.csv 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.Folders containing the images are named according to the respective image size. Image files are named based on the mapillary_image_id. You can find the corresponding publication here: StreetSurfaceVis: a dataset of crowdsourced street-level imagery with semi-automated annotations of road surface type and quality Image metadata Each CSV record contains information about one street-level image with the following attributes: mapillary_image_id: ID provided by Mapillary (see information below on Mapillary) user_id: Mapillary user ID of contributor user_name: Mapillary user name of contributor captured_at: timestamp, capture time of image longitude, latitude: location the image was taken at train: 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. surface_type: 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 surface_quality: 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 Labeling Guide document for details) Image source Images are obtained from Mapillary, a crowd-sourcing plattform for street-level imagery. More metadata about each image can be obtained via the Mapillary API . User-generated images are shared by Mapillary under the CC-BY-SA License. For each image, the dataset contains the mapillary_image_id and user_name. You can access user information on the Mapillary website by https://www.mapillary.com/app/user/ and image information by https://www.mapillary.com/app/?focus=photo&pKey= If you use the provided images, please adhere to the terms of use of Mapillary. Instances per class Total number of images: 9,122 excellent good intermediate bad very bad asphalt 971 1697 821 246 - concrete 314 350 250 58 - paving stones 385 1063 519 70 - sett - 129 694 540 - unpaved - - 326 387 303 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. Number of test images (train-test split): 776 Inter-rater-reliablility 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.1,800 images were annotated by all three annotators, resulting in a Krippendorff's alpha of 0.96 for surface type and 0.74 for surface quality. Recommended image preprocessing 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. This is an exemplary code for recommended image preprocessing in Python: from PIL import Imageimg = Image.open(image_path)width, height = img.sizeimg_cropped = img.crop((0.25 * width, 0.5 * height, 0.75 * width, height)) License CC-BY-SA Citation If you use this dataset, please cite as: Kapp, A., Hoffmann, E., Weigmann, E. et al. StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality. Sci Data 12, 92 (2025). https://doi.org/10.1038/s41597-024-04295-9 @article{kapp_streetsurfacevis_2025, title = {{StreetSurfaceVis}: a dataset of crowdsourced street-level imagery annotated by road surface type and quality}, volume = {12}, issn = {2052-4463}, url = {https://doi.org/10.1038/s41597-024-04295-9}, doi = {10.1038/s41597-024-04295-9}, pages = {92}, number = {1}, journaltitle = {Scientific Data}, shortjournal = {Scientific Data}, author = {Kapp, Alexandra and Hoffmann, Edith and Weigmann, Esther and Mihaljević, Helena}, date = {2025-01-16},} ----------------------------------------------------------------------------------------------------------------------------------------------------------- This is part of the SurfaceAI project at the University of Applied Sciences, HTW Berlin. - Prof. Dr. Helena Mihajlević- Alexandra Kapp- Edith Hoffmann- Esther Weigmann Contact: surface-ai@htw-berlin.de https://surfaceai.github.io/surfaceai/ Funding: SurfaceAI is a mFund project funded by the Federal Ministry for Digital and Transportation Germany.