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Evaluating segmentation methods for UAV-Based Spoil Pile Delineation.

Sureka Thiruchittampalam1,2, Bikram Pratap Banerjee3, Nancy F Glenn4

  • 1School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW, 2052, Australia.

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|March 26, 2025
PubMed
Summary
This summary is machine-generated.

This study compares image segmentation methods for characterizing mine waste dumps. The Segment Anything Model (SAM) using deep learning and morphology showed the best results for identifying individual spoil piles.

Keywords:
Mean shift segmentationSegment anything modelSimple linear iterative clusteringStarDist segmentationVoronoi-based segmentation

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Area of Science:

  • Geotechnical Engineering
  • Remote Sensing
  • Computer Vision

Background:

  • Mine waste dumps are composed of individual spoil piles with varying geological and geotechnical properties.
  • Manual characterization of these spoil piles is challenging due to accessibility, safety, and time constraints.
  • Object-based image classification offers a potential solution for identifying and characterizing spoil piles using remote sensing data.

Purpose of the Study:

  • To identify and compare different image segmentation approaches for spoil pile characterization.
  • To evaluate the effectiveness of traditional versus deep learning-based segmentation methods.
  • To establish optimal segmentation strategies for image-based spoil pile analysis.

Main Methods:

  • Comparative analysis of traditional segmentation techniques.
  • Evaluation of deep learning-based segmentation methodologies.
  • Assessment of the Segment Anything Model (SAM) incorporating morphological data.

Main Results:

  • The Segment Anything Model (SAM), a deep learning approach utilizing morphological data, demonstrated superior performance in segmenting spoil piles.
  • Traditional segmentation methods were less effective compared to deep learning approaches.
  • Accurate segmentation is crucial for the success of object-based classification in spoil pile analysis.

Conclusions:

  • Deep learning techniques, particularly SAM with morphological data, offer an effective and efficient solution for spoil pile segmentation.
  • Optimized segmentation strategies enhance the application of image-based monitoring for mine dumps.
  • This research contributes to sustainable and hazard-free management of mine dump environments.