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.

翻译：煤矸石堆积过程中的微小误差（例如将具有较高抗剪强度的较硬材料置于较软材料之上）可能导致排土场失稳。非规则堆积与压实不足加剧了煤矸石性状的复杂性，亟需建立稳健的时间监测方法。本研究探索采用配备红绿蓝（RGB）传感器的无人机（UAV）实现高效数据采集。尽管无人机数据广泛使用，但原始数据存在时间不一致性，阻碍了对时序变化的精确评估。这是由于基于无人机的传感存在辐射误差，其成因包括传感器噪声、大气散射与吸收、太阳参数变化以及感知对象随时间变化的特性。为此，本研究引入基于不变目标的经验线校准方法，实现跨场景的精确标定。校准后的RGB数据展现出显著性能优势，采用集成（子空间判别）分类器对煤矸石堆进行分类时，总体精度达到90.7%，较未校准数据分类结果提升了7%。本研究凸显了数据校准在优化无人机时空矿堆监测效能中的关键作用。所开发的校准工作流在多时相数据中表现出稳健性和可靠性。这些发现将为矿山废弃物管理领域可持续实践方法的优化提供关键依据。