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.

Question Text Classification Method of Tourism Based on Deep Learning Model

“…As an open source framework, Tensorflow integrates several models including convolutional neural network, RNN network, and LSTM model [25]. The emergence of Tensorflow framework makes the use of deep learning model simpler and convenient and reduces the difficulty of applying deep learning model [26]. The specific experimental environment is shown in Table 1.…”