Granular characterisation of coal spoil dump using unmanned aerial vehicle data to enhance stability analysis

Open pit mining operations generate significant spoil dumps that need to be characterised for stability to identify potentially unstable slopes. However, the current subjective practice for spoil characterisation often involves tedious and risky field work. To this end, this study demonstrated the use of periodically acquired unmanned aerial vehicle (UAV)-based images over a coal mine spoil dump in New South Wales, Australia. A granular approach that captures the variability of each truck offload pile on a dump was adopted through morphology-based segmentation and ensemble algorithm-based classification which consolidates predictions from multiple classifiers. Overall accuracy of over 90% in the material characterisation based on the classification framework was achieved. The two-dimensional classification outcome was then transformed into three-dimensional (3D) block models using a point-based interpolation approach for stability analysis. The factor of safety derived from the granular approach offered improved assessment of failure risk compared to the conventional approaches, which treat the entire dump as a uniform category. This rapid classification and assessment method proposed in this study will help reduce the uncertainty associated with the variability of spoil dumps in slope stability assessments, thereby enhancing the safety and efficiency of mining operations.