The stability of mine dumps is contingent upon the precise arrangement of
spoil piles, taking into account their geological and geotechnical attributes.
Yet, on-site characterisation of individual piles poses a formidable challenge.
The utilisation of image-based techniques for spoil pile characterisation,
employing remotely acquired data through unmanned aerial systems, is a
promising complementary solution. Image processing, such as object-based
classification and feature extraction, are dependent upon effective
segmentation. This study refines and juxtaposes various segmentation
approaches, specifically colour-based and morphology-based techniques. The
objective is to enhance and evaluate avenues for object-based analysis for
spoil characterisation within the context of mining environments. Furthermore,
a comparative analysis is conducted between conventional segmentation
approaches and those rooted in deep learning methodologies. Among the diverse
segmentation approaches evaluated, the morphology-based deep learning
segmentation approach, Segment Anything Model (SAM), exhibited superior
performance in comparison to other approaches. This outcome underscores the
efficacy of incorporating advanced morphological and deep learning techniques
for accurate and efficient spoil pile characterisation. The findings of this
study contribute valuable insights to the optimisation of segmentation
strategies, thereby advancing the application of image-based techniques for the
characterisation of spoil piles in mining environments.

基于图像的矿山废弃物堆积物表征的优化，利用远程获取的数据和深度学习方法，通过各种分割方法的比较分析，展示了基于形态学和深度学习方法的高准确性和高效率，推动了矿山环境中基于图像的堆积物表征技术的应用。