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Related Experiment Video

Updated: Oct 13, 2025

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Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand.

Bo Liu1,2, Bin Yang1, Sina Masoud-Ansari3

  • 1School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary

This study introduces an automated method for classifying beach states using over 20 years of coastal images and tidal data. An improved convolutional neural network (CNN) model accurately identifies beach morphology changes, aiding coastal management.

Keywords:
beach state classificationcoastal imageconvolutional neural networkspattern recognition

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Area of Science:

  • Coastal geomorphology and morphodynamics
  • Remote sensing and image analysis
  • Machine learning applications in environmental science

Background:

  • Coastal processes are vital for infrastructure and property protection, often studied using remote sensing data.
  • Existing beach state classification methods face limitations in data availability and operator objectivity.
  • Long-term datasets capturing diverse beach states are rare, hindering comprehensive analysis.

Purpose of the Study:

  • To develop an objective and automated classification system for diverse beach states using long-term coastal imagery.
  • To analyze temporal patterns and characteristics of coastal morphology changes.
  • To enhance understanding of morphodynamic state transitions.

Main Methods:

  • Collected over 20 years of hourly coastal images and tidal data (November 1998-August 2019).
  • Classified images into eight classic beach states using a convolutional neural network (CNN) model, specifically an improved ResNext architecture.
  • Applied data enhancement for pre-processing and MDLats algorithms for temporal pattern analysis.

Main Results:

  • The improved ResNext CNN model achieved a 90.41% F1-score, demonstrating high accuracy and generalization ability in classifying beach states.
  • Classification results were converted to time series data to identify frequent temporal patterns in morphology changes.
  • Analysis revealed characteristics of beach morphology and transitions in morphodynamic states.

Conclusions:

  • Automated CNN-based classification provides an objective and efficient method for analyzing long-term beach evolution.
  • The study successfully identified temporal patterns in coastal morphology changes, linking them to tidal data and morphodynamic states.
  • This approach offers valuable insights for coastal protection, management, and development strategies.