Underground Parking Lot Car Finder Image Dataset

#Image recognition #object detection #navigation optimization #Intelligent parking lots #vehicle navigation systems #traffic management
  • 500 records
  • 1.3G
  • JPG
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

Currently, with the continuous growth of urbanization and the number of cars owned, underground parking lots, as an important part of urban infrastructure, face challenges in parking space management and quick car location. Existing manual inspections and simple navigation signs are inefficient and error-prone, unable to meet the increasing parking demand and the requirement to enhance user experience. The Underground Parking Lot Car Finder Image Dataset aims to solve technical issues such as the difficulty in vehicle positioning and long car-finding times through the application of image recognition and navigation technology, fulfilling the business needs of smart parking lots for intelligent and refined management. Data collection uses professional high-definition camera equipment to capture parking lot environment images under different lighting and weather conditions. Through multiple rounds of strict manual annotation, consistency checks, and expert review for quality control, high accuracy and consistency of data are ensured. The annotation team consists of experts in the fields of traffic engineering and computer vision, with a team size of over 50 people. During data preprocessing, techniques such as denoising, contrast adjustment, and image enhancement are applied to improve data quality. Data is stored in JPG format, organized for easy query and analysis by location and date.

Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
license_plate_numberstringThe vehicle's license plate number.
parking_space_numberstringThe identifier of the parking space where the vehicle is located.
directionstringThe direction of the vehicle in the parking lot, such as forward or backward.
vehicle_colorstringThe exterior color of the vehicle.
lighting_conditionsstringThe lighting conditions displayed in the image, such as bright or dim.
vehicle_typestringThe type of vehicle, such as a sedan, SUV, etc.
obstruction_levelstringWhether the vehicle is obstructed by other objects and the degree of obstruction.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the Underground Parking Lot Car Finder Image Dataset?
The Underground Parking Lot Car Finder Image Dataset is a collection of images from underground parking lots, aimed at optimizing smart parking navigation.
What are the applications of the Underground Parking Lot Car Finder Image Dataset?
This dataset can be used for developing and optimizing parking space navigation systems, space detection, and improving parking experiences.
Which fields are suitable for the Underground Parking Lot Car Finder Image Dataset?
This dataset is mainly suitable for the transportation and driving fields, especially in developing smart parking solutions.
What are the advantages of using the Underground Parking Lot Car Finder Image Dataset?
Using this dataset can improve the accuracy of vehicle detection and navigation systems, thereby enhancing parking management efficiency and user parking experience.

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Cite this Work

@dataset{Mobiusi2026,
  title={Underground Parking Lot Car Finder Image Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/a93e01145668d18eea33da2394769025},
  urldate={2026-02-04},
  keywords={Underground parking lot dataset, car finder image, smart driving image data, parking lot navigation optimization},
  version={1.0}
}

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