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Related Experiment Video

Updated: Sep 4, 2025

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
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See the wind: Wind scale estimation with optical flow and VisualWind dataset.

Qin Zhang1, Jialang Xu2, Matthew Crane3

  • 1Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK; College of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.

The Science of the Total Environment
|July 15, 2022
PubMed
Summary

This study trains cameras to estimate wind scale using video analysis, achieving 86.69% accuracy. The novel approach leverages optical flow and machine learning to analyze tree motion, enhancing weather data resolution.

Keywords:
Motion featureOptical flowVisual featureWind scale estimation

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Area of Science:

  • Computer Vision
  • Environmental Science
  • Machine Learning

Background:

  • Professional weather records often lack fine-grained spatiotemporal resolution.
  • Human interpretation of wind scale relies on observing environmental object dynamics, particularly trees.
  • Existing methods for wind sensing are limited in scope and resolution.

Purpose of the Study:

  • To develop a camera-based system for sensing wind scale using video analysis.
  • To improve the spatiotemporal resolution of weather data through automated wind scale estimation.
  • To create a comprehensive dataset for training and evaluating wind sensing models.

Main Methods:

  • Introduction of a novel dataset with over 6000 labeled video clips of trees swaying in eleven Beaufort wind classes.
  • Proposal of a dual-branch model incorporating an optical flow-based motion branch and a visual branch.
  • Adaptive fusion of motion and visual information for wind scale estimation.

Main Results:

  • Achieved 86.69% accuracy in estimating wind scale from video clips.
  • Demonstrated superior performance compared to baseline models, including a two-stage model and a motion-only branch model.
  • The dual-branch model effectively utilizes both motion dynamics and visual cues.

Conclusions:

  • The proposed method effectively enables cameras to sense wind scale by analyzing tree motion.
  • The developed dataset and model offer a significant advancement in high-resolution wind sensing.
  • Publicly accessible dataset and code facilitate further research and application in environmental monitoring.