Urban Lab

Urban Classes detector

Object Detection

Roboflow Universe Urban Lab Urban Classes detector

Urban Classes detector Computer Vision Project


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Here are a few use cases for this project:

  1. Traffic Analysis: The Urban Classes detector can be used by city planners or traffic management centres to analyze vehicular and pedestrian traffic patterns, identify peak times, and develop better road safety measures.

  2. Surveillance System: Security firms or law enforcement agencies can use this model to improve their surveillance systems by creating alerts for certain classes such as a large gathering of people, unusual activities involving bikes, skateboards, etc.

  3. Autonomous Vehicle Development: Car manufacturers could employ this detector model to improve the recognition and differentiation capabilities of autonomous cars in urban settings.

  4. Urban Planning: This model can be used by urban planners to assess the need for infrastructure development like bike lanes, pedestrian areas, or motor vehicle lanes based on the ratio of persons, bikes, cars, and skateboards.

  5. Retail Economy Research: Businesses and retail centres could use the data to analyze urban mobility patterns for market research and business strategy, such as determining the best times to operate or identifying the most popular means of transport for potential customers.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ urban-classes-detector_dataset,
    title = { Urban Classes detector Dataset },
    type = { Open Source Dataset },
    author = { Urban Lab },
    howpublished = { \url{ https://universe.roboflow.com/urban-lab/urban-classes-detector } },
    url = { https://universe.roboflow.com/urban-lab/urban-classes-detector },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jun },
    note = { visited on 2023-12-08 },

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Urban Lab

Last Updated

5 months ago

Project Type

Object Detection




bicycle, bus, car, motorbike, pedestrian, person, sit-ped, sitting person, sitting-ped, sitting-person, skateboarder, van

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