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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Sep 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Expert system design for vacant parking space location using automatic learning and artificial vision.

Juan Manuel Carrera García1, Joaquín Recas Piorno1, María Guijarro Mata-García1

  • 1UCM: Universidad Complutense de Madrid, Madrid, Spain.

Multimedia Tools and Applications
|May 2, 2022
PubMed
Summary

This study introduces an AI-powered system using zenith images to automatically detect and monitor parking space occupancy in real-time. The technology accurately identifies available spots, improving parking efficiency and user experience.

Keywords:
Computer visionMachine learningNeural networkParking lot assistance

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Smart City Solutions

Background:

  • Urban parking scarcity is a significant challenge in densely populated areas.
  • Efficient management of public parking lots is crucial for traffic flow and driver convenience.

Purpose of the Study:

  • To develop an automated system for real-time parking space detection and occupancy monitoring.
  • To enhance parking lot management through advanced image analysis and artificial intelligence.

Main Methods:

  • Utilizing zenith images for parking lot analysis.
  • Employing image processing techniques (filtering, thresholding, contour extraction, polygon approximation) to map parking spaces.
  • Implementing Region-based Convolutional Neural Networks (R-CNNs) for vehicle detection and occupancy analysis with 98.21% accuracy.

Main Results:

  • Successful semi-automatic detection and mapping of parking spaces in an empty lot.
  • Accurate real-time identification of vehicle presence and occupied area within parking spaces.
  • Capability to recommend suitable parking spots for incoming vehicles based on dimensions and adjacent vehicle locations.

Conclusions:

  • The developed system offers an effective solution for automated parking management.
  • Real-time occupancy detection and precise space mapping significantly improve parking lot efficiency.
  • This technology can guide drivers to appropriate spaces, reducing search times and congestion.