  The characterisation of materials is a prerequisite for evaluating and
predicting the stability of mining waste dumps. Over the past three decades,
the BHP Mitsubishi Alliance Coal framework has been a cornerstone in Australian
coal mines for characterising waste dump materials. However, its reliance on
subjective human observations has introduced potential inaccuracies and
subjectivity into the process. In response to these limitations, this study
proposes an innovative approach to classify coal spoil attributes by remotely
acquiring images through phones/tablets. Automated image-based classification
relies on feature extraction and a substantial amount of data. Nevertheless,
the inherent complexity of geological factors contributing to the formation of
both rare and dominant materials leads to imbalanced data. Recognising the need
for classification mechanisms to overcome these challenges in spoil
classification, the study explores and compares the use of convolutional neural
networks, hybrid deep learning, and traditional techniques. Among the twelve
models evaluated in this study, the ResNet18-k nearest neighbour model emerges
as a powerful tool in geotechnical characterisation. However, it is essential
to address issues of interpretability and adaptability to diverse datasets. As
this study evolves, the field of geotechnical characterisation of spoil can
anticipate the development of more robust methods in the future.
