  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.
