The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line. Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil offloading continues over time. Generally, the conventional in-situ coal spoil characterisation is inefficient, laborious, hazardous, and prone to experts' observation biases. To this end, this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle (UAV) based optical remote sensing. The textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters (i.e., fabric structure and relative density/consistency) in the proposed workflow. The raw images were converted to an orthomosaic using structure from motion aided processing. Then, structural descriptors were computed per pixel to enhance feature modalities of the spoil materials. Finally, machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention. The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

Minor errors in the spoil deposition process, such as placing stronger materials with higher shear strength over weaker ones, can lead to potential dump failure. Irregular deposition and inadequate compaction complicate coal spoil behaviour, necessitating a robust methodology for temporal monitoring. This study explores using unmanned aerial vehicles (UAV) equipped with red–green–blue (RGB) sensors for efficient data acquisition. Despite their prevalence, raw UAV data exhibit temporal inconsistency, hindering accurate assessments of changes over time which could be attributed to radiometric errors. To this end, the study introduces an empirical line calibration with invariant targets (ELC-IT), for precise calibration across diverse scenes, particularly in the context of UAV imagery used to monitor the evolving nature of spoil dumps. To evaluate the effectiveness of this calibration approach, accuracy assessment of an object-based classification is conducted on both calibrated and uncalibrated data. This classification involves several steps: performing segmentation, carrying out feature extraction, and integrating the extracted features and ground truth labels collected over the time period of UAV image capture into machine learning pipelines. Calibrated RGB data exhibit a substantial performance advantage, achieving a 90.7% overall accuracy for spoil pile classification using ensemble (subspace discriminant), representing a noteworthy 7% improvement compared to classifying uncalibrated data. The study highlights the critical role of data calibration in optimising UAV effectiveness for spatio-temporal mine dump monitoring. These findings play a crucial role in informing and refining sustainable management practices within the domain of mine waste management.

Minor errors in the spoil deposition process, such as placing stronger materials with higher shear strength over weaker ones, can lead to potential dump failure. Irregular deposition and inadequate compaction complicate coal spoil behaviour, necessitating a robust methodology for temporal monitoring. This study explores using unmanned aerial vehicles (UAV) equipped with red-green-blue (RGB) sensors for efficient data acquisition. Despite their prevalence, raw UAV data exhibit temporal inconsistency, hindering accurate assessments of changes over time. This is attributed to radiometric errors in UAV-based sensing arising from factors such as sensor noise, atmospheric scattering and absorption, variations in sun parameters, and variable characteristics of the sensed object over time. To this end, the study introduces an empirical line calibration with invariant targets, for precise calibration across diverse scenes. Calibrated RGB data exhibit a substantial performance advantage, achieving a 90.7% overall accuracy for spoil pile classification using ensemble (subspace discriminant), representing a noteworthy 7% improvement compared to classifying uncalibrated data. The study highlights the critical role of data calibration in optimising UAV effectiveness for spatio-temporal mine dump monitoring. The developed calibration workflow proves robust and reliable across multiple dates. Consequently, these findings play a crucial role in informing and refining sustainable management practices within the domain of mine waste management.

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.

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