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Related Concept Videos

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

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 served as...
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Updated: May 24, 2026

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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Meteorological Visibility Estimation Using Landmark Object Extraction and the ANN Method.

Wai-Lun Lo1, Kwok-Wai Wong1, Richard Tai-Chiu Hsung1

  • 1Department of Computer Science, Hong Kong Chu Hai College, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, New Territories, Hong Kong, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for selecting key image regions to improve visibility estimation accuracy. By using landmark object extraction, the approach enhances environmental monitoring and air quality assessment.

Keywords:
artificial neural networklandmark object extractionmeteorological visibility estimation

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Area of Science:

  • Environmental Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visibility estimation is crucial for environmental monitoring, including weather and air pollution.
  • Traditional methods rely on meteorological laws or image analysis, but are sensitive to image quality and noise.
  • Existing AI approaches use image features but often require manual selection of relevant image subregions.

Purpose of the Study:

  • To develop an automated method for selecting effective subregions for visibility estimation.
  • To improve the accuracy and efficiency of visibility estimation using artificial intelligence.
  • To reduce redundant information in image data for more robust AI model training.

Main Methods:

  • Proposed an automatic effective subregion selection method utilizing landmark object extraction techniques.
  • Extracted image features from identified landmark object (LMO) subregions.
  • Employed an Artificial Neural Network (ANN) to map LMO features to visibility values.

Main Results:

  • The automated subregion selection minimized redundant information for ANN training.
  • Achieved improved accuracy in visibility estimation compared to single-image approaches.
  • Demonstrated the effectiveness of landmark object extraction for targeted feature generation.

Conclusions:

  • The proposed method offers a more accurate and automated approach to visibility estimation.
  • Landmark object extraction is a viable technique for identifying critical image areas for environmental analysis.
  • This advancement has implications for enhanced environmental monitoring and air quality assessment.