Using UAV for automatic lithological classification of open pit mining front

Abstract Mine planning is dependent on the natural lithologic features and on the definition of their limits. The geological model is constantly updated during the life of the mine, based on all the information collected so far, plus the knowledge developed from the exploration stage up to the mine closure. As the mine progresses, the amount of available data increases, as well as the experience of the geological modeller and mine planner who deliver the short, medium, and long-term plans. This classical approach can benefit from the automation of the geological mapping on the mining faces and outcrops, improving the speed of repetitious work and avoiding exposure to intrinsic dangers like mining equipment, falling rocks, high wall proximity, among others. The use of photogrammetry to keep up with surface mining activities boarded in UAVs is a reality and the automated lithological classification using machine learning techniques is a low-cost evolution that might present accuracies above 90% of the contact zones and lithologies based on the automated dense point cloud classification when compared to the manual (or reality) classified model.摘要：矿山规划的制定依赖于天然岩性特征及其边界的界定。地质模型（Geological Model）在矿山全生命周期内会持续更新，其更新依据为迄今收集的全部信息，以及从勘探阶段直至矿山闭坑阶段所积累的知识。随着矿山开采推进，可用数据量不断增长，同时负责制定短期、中期与长期规划的地质建模师与矿山规划师的经验也愈发丰富。该经典方法可借助采矿工作面与露头的地质测绘自动化获得优化：不仅能提升重复性工作的效率，还可避免作业人员暴露于采矿设备、落石、近距高边坡等固有危险中。搭载于无人机（UAV）的摄影测量（Photogrammetry）技术用于跟进露天采矿作业已成为现实；而采用机器学习技术实现自动化岩性分类则是一种低成本的升级方案：相较于人工（或实地）分类模型，基于自动化密集点云分类的岩性与接触带识别准确率可达到90%以上。