Over the past decade, unmanned aerial vehicles (UAVs) have been used in the mining industry for various applications from mineral exploration to mine reclamation. This study aims to review academic papers on the applications of UAVs in mining by classifying the mining process into three phases: exploration, exploitation, and reclamation. Systematic reviews were performed to summarize the results of 65 articles (June 2010 to May 2020) and outline the research trend for applying UAVs in mining. This study found that UAVs are used at mining sites for geological and structural analysis via remote sensing, aerial geophysical survey, topographic surveying, rock slope analysis, working environment analysis, underground surveying, and monitoring of soil, water, ecological restoration, and ground subsidence. This study contributes to the classification of current UAV applications during the mining process as well as the identification of prevalent UAV types, data acquired by sensors, scales of targeted areas, and styles of flying control for the applications of UAVs in mining.

Since Microsoft HoloLens first appeared in 2016, HoloLens has been used in various industries, over the past five years. This study aims to review academic papers on the applications of HoloLens in several industries. A review was performed to summarize the results of 44 papers (dated between January 2016 and December 2020) and to outline the research trends of applying HoloLens to different industries. This study determined that HoloLens is employed in medical and surgical aids and systems, medical education and simulation, industrial engineering, architecture, civil engineering and other engineering fields. The findings of this study contribute towards classifying the current uses of HoloLens in various industries and identifying the types of visualization techniques and functions.

In this study, geographic information system (GIS)-based methods and applications utilized for mine development were reviewed. Three types of GIS-based studies, namely studies on mine planning, operation, and environmental management, were examined to describe the role of GIS as a decision-making support tool in mine development. This review was conducted by classifying previous GIS-based studies into several subtopics that pertain to mine development activities and the range of environments to be managed. Because the use of GIS is appropriate for spatial data management related to ore deposits and mine environment conditions at various scales, the applications of GIS-based methods in mine development could be expanded further.

This study proposes a method for diagnosing problems in truck ore transport operations in underground mines using four machine learning models (i.e., Gaussian naïve Bayes (GNB), k-nearest neighbor (kNN), support vector machine (SVM), and classification and regression tree (CART)) and data collected by an Internet of Things system. A limestone underground mine with an applied mine production management system (using a tablet computer and Bluetooth beacon) is selected as the research area, and log data related to the truck travel time are collected. The machine learning models are trained and verified using the collected data, and grid search through 5-fold cross-validation is performed to improve the prediction accuracy of the models. The accuracy of CART is highest when the parameters leaf and split are set to 1 and 4, respectively (94.1%). In the validation of the machine learning models performed using the validation dataset (1500), the accuracy of the CART was 94.6%, and the precision and recall were 93.5% and 95.7%, respectively. In addition, it is confirmed that the F1 score reaches values as high as 94.6%. Through field application and analysis, it is confirmed that the proposed CART model can be utilized as a tool for monitoring and diagnosing the status of truck ore transport operations.

In this study, a mine production management application (app) using a Bluetooth beacon and tablet PC was developed to support the efficient operation of an underground mine loading-transport system. The app receives signals from the Bluetooth beacons attached to major loading points and crushing sites through the tablet PC mounted on the truck and records the time the signals were received as well as the location of the truck. In addition, when the tablet PC receives the signal from the Bluetooth beacon, the truck driver can select and input information such as loading point, ore type, and dumping method on the tablet PC screen. Data recorded on the tablet PC during the haulage operation are automatically transmitted to the cloud server when the truck arrives in a wireless communication area. The cloud server continuously stores and manages data transmitted from multiple trucks equipped with tablet PCs. The performance test was conducted by using the system developed for a limestone underground mine located in Jeongseon, Korea. Results confirmed that the information related to ore production in the field could be effectively collected and managed, and the efficiency of production management could be improved.

This study proposes a novel approach for enhancing the productivity of mining haulage systems by developing a hybrid model that combines machine learning (ML) and discrete event simulation (DES) techniques to predict ore production. This study utilized time data collected from a limestone underground mine using tablet computers and Bluetooth beacons for 15 weeks. The collected data were used to train an ML model to predict truck cycle time, and the support vector regression with particle swarm optimization (PSO–SVM) model demonstrated the best performance. The PSO–SVM model accurately predicted cycle time with a mean absolute error (MAE) of 2.79 min, mean squared error (MSE) of 14.29 min², root mean square error (RMSE) of 3.79 min, and coefficient of determination (R²) of 0.68. The output of the ML model was linked to the DES model to predict ore production for each truck, section, and time period. Verification of the DES model demonstrated its ability to accurately simulate the haulage system in the study area by comparing production logs with the simulation results. This study’s novel approach offers a new method for predicting ore production and determining the optimal equipment combination for each workplace, thus enhancing productivity in mining haulage systems.