High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

Identifying the as-drilled location of blastholes is crucial for achieving optimal blasting results. This research proposes a novel integrated methodology to control drilling accuracy in open-pit mines. This approach is developed by combining aerial drone images with machine learning techniques. The study investigates the viability of photogrammetry combined with machine learning techniques, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), for automatically detecting blastholes in photogrammetry representations of blast patterns. To verify the hypothesis that machine learning can detect blastholes in images as effectively as humans, various datasets (drone images) were obtained from different mine sites in Nevada, USA. The images were processed to create photogrammetry mapping of the drill patterns. In this process, thousands of patches were extracted and augmented from the photogrammetry representations. Those patches were then used to train and test different CNN architectures optimized to locate blastholes. After reaching an acceptable level of accuracy during the training process, the model was tested using a piece of completely unknown data (testing dataset). The high recall, precision, and percentage of detected blastholes prove that the combination of SVM, CNN, and photogrammetry (PHG) is an effective methodology for detecting blastholes on photogrammetry maps.