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Street Test

## Overview

Street Test is a dataset for instance segmentation tasks - it contains Test annotations for 691 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset provides information about the number of properties, residents, and average property values for Checker Road cross streets in Long Grove, IL.

This dataset provides information about the number of properties, residents, and average property values for Double Springs Road cross streets in Check, VA.

This dataset provides information about the number of properties, residents, and average property values for Diamond Knob Road cross streets in Check, VA.

Validity check.

Chesapeake street centerlines represented as vector lines. VDOT_Rank field uses: LOS-LOCAL, COL-COLLECTOR, ARP-ARTERIAL PRIMARY, ARM-ARTERIAL MINOR. TYPE_ field: Street type follows U.S. Postal standards. LEFT_IS and RIGHT_IS fields use: O-odd numbers, E-even numbers, B- both odd and even numbers. ONEWAY field: BI- bidirectional, FT- From-to, TF- To-from

This dataset provides information about the number of properties, residents, and average property values for Checker Court cross streets in Apex, NC.

The Street View House Numbers (SVHN) dataset is a dataset of 604,300 images of house numbers taken from Google Street View. The dataset is split into a training set of 73,257 images, a test set of 26,032 images, and a validation set of 50,113 images. The images in the dataset are all 32 x 32 pixels in size and are in grayscale. The dataset is used to train and evaluate machine learning models for the task of digit recognition.

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at August 2022 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England and Wales. Scotland and Northern Ireland has the 2011 Census Output AreasIt supports the production of area based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 184 MB).

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

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 Image
img = Image.open(image_path)
width, height = img.size
img_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},
}

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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.

All street names approved in Wake County. Data includes the date the street was approved, what jurisdiction it was approved by, and the name of the subdivision it is part of. Data is updated nightly and includes newly approved street names that are not currently developed. Data is used in the Street Name Dashboard which shows statistics on streets by year approved, jurisdiction, subdivision, and street type. Dataset is also used to check if a street name is already in use in the online Street Name Application.

description: Displays all non-restricted local road segments in the state of Iowa. Specific route information is only available once zoomed into the county level. County restriction map can be found here: http://www.iowadot.gov/mvd/motorcarriers/systemmap.htm#county Check with local officials when traveling on county roads or city streets for bridge embargo, detour, road embargo and vertical clearance information. State and interstate highways are valid routes except for restrictions listed on the bridge embargo, detour and road embargo, and vertical clearance maps located at http://www.iowadot.gov/mvd/motorcarriers/omcsMaps.htm.; abstract: Displays all non-restricted local road segments in the state of Iowa. Specific route information is only available once zoomed into the county level. County restriction map can be found here: http://www.iowadot.gov/mvd/motorcarriers/systemmap.htm#county Check with local officials when traveling on county roads or city streets for bridge embargo, detour, road embargo and vertical clearance information. State and interstate highways are valid routes except for restrictions listed on the bridge embargo, detour and road embargo, and vertical clearance maps located at http://www.iowadot.gov/mvd/motorcarriers/omcsMaps.htm.

This dataset provides information about the number of properties, residents, and average property values for Shawsville Pike cross streets in Check, VA.

This is the ONS Postcode Directory (ONSPD) for the United Kingdom as at February 2024 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. This file contains the multi CSVs so that postcode areas can be opened in MS Excel. To download the zip file click the Download button. The ONSPD relates both current and terminated postcodes in the United Kingdom to a range of current statutory administrative, electoral, health and other area geographies. It also links postcodes to pre-2002 health areas, 1991 Census enumeration districts for England and Wales, 2001 Census Output Areas (OA) and Super Output Areas (SOA) for England and Wales, 2001 Census OAs and SOAs for Northern Ireland and 2001 Census OAs and Data Zones (DZ) for Scotland. It now contains 2021 Census OAs and SOAs for England, Wales and Northern Ireland. It helps support the production of area-based statistics from postcoded data. The ONSPD is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The ONSPD is issued quarterly. (File size - 231 MB) Please note that this product contains Royal Mail, Gridlink, LPS (Northern Ireland), Ordnance Survey and ONS Intellectual Property Rights.

This dataset provides information about the number of properties, residents, and average property values for Conners Trail cross streets in Check, VA.

Traffic Checker

## Overview

Traffic Checker is a dataset for object detection tasks - it contains Street Objects And Person annotations for 574 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

PHL Open Data Testing

This data set was acquired by the USDOT Data Capture and Management program. The purpose of the data set is to provide multi-modal data and contextual information (weather and incidents) that can be used to research and develop applications. Contains one full year (January – December 2010) of raw 30-second data for over 3,000 traffic detectors deployed along 1,250 lane miles of monitored roadway in San Diego. Cleaned and geographically referenced data for over 1,500 incidents and lane closures for the two sections of I-5 that experienced the greatest number of incidents during 2010. Complete trip (origin-to-destination) GPS “breadcrumbs” collected by ALK Techonologies, containing latitude/longitude, vehicle heading and speed data, and time for individual in-vehicles devices updated at 3-second intervals for over 10,000 trips taken during 2010. A digital map shape file containing ALK’s street-level network data for the San Diego Metropolitan area. And San Diego Weather data for 2010. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.