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Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images.

Kazi Aminul Islam1, Omar Abul-Hassan2, Hongfang Zhang3

  • 1Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA.

Geomatics (Basel, Switzerland)
|September 15, 2025
PubMed
Summary
This summary is machine-generated.

Automated bathymetry mapping using machine learning on satellite images reduces costs and improves data accessibility. Optimized CatBoostOpt model accurately estimates water depth using WorldView-2 imagery, outperforming other methods.

Keywords:
CatBoostbathymetrygradient boostingmachine learningmulti-spectral images

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Machine Learning

Background:

  • Traditional bathymetry methods are labor-intensive and yield incomplete data.
  • Automated bathymetry estimation is crucial for cost reduction and broader research application.

Purpose of the Study:

  • To optimize the CatBoostOpt machine learning model for bathymetry estimation using WorldView-2 satellite imagery.
  • To evaluate different data transformations and spectral bands for improved bathymetric mapping accuracy.

Main Methods:

  • Applied the CatBoostOpt model to WorldView-2 multi-spectral satellite images.
  • Correlated in situ sonar bathymetry data with satellite reflectance values.
  • Evaluated raw reflectance, log-linear, and log-ratio transformations, and assessed individual spectral band contributions.

Main Results:

  • CatBoostOpt with log-ratio transformed reflectance achieved the highest accuracy.
  • The model demonstrated a root mean square error (RMSE) of 0.34 and R-squared of 0.87.
  • Utilizing all eight spectral bands of WorldView-2 imagery provided the best results for complex water conditions.

Conclusions:

  • Optimized CatBoostOpt model offers a cost-effective and accurate solution for automated bathymetry mapping.
  • Machine learning applied to multi-spectral satellite data can overcome limitations of traditional bathymetric surveys.
  • The study highlights the potential of satellite-derived bathymetry for coastal research and management.