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.

This paper reviews current trends in wearable device technology, and provides an overview of its prevalent and potential deployments in the mining industry. This review includes the classification of wearable devices with some examples of their utilization in various industrial fields as well as the features of sensors used in wearable devices. Existing applications of wearable device technology to the mining industry are reviewed. In addition, a wearable safety management system for miners and other possible applications are proposed. The findings of this review show that by introducing wearable device technology to mining sites, the safety of mining operations can be enhanced. Therefore, wearable devices should be further used in the mining industry.

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.

Recently, the mining industry has introduced renewable energy technologies to resolve power supply problems at mines operating in polar regions or other remote areas, and to foster substitute industries, able to benefit from abandoned sites of exhausted mines. However, little attention has been paid to the potential placement of floating photovoltaic (PV) systems operated on mine pit lakes because it was assumed that the topographic characteristics of open-pit mines are unsuitable for installing any type of PV systems. This study analyzed the potential of floating PV systems on a mine pit lake in Korea to break this misconception. Using a fish-eye lens camera and digital elevation models, a shading analysis was performed to identify the area suitable for installing a floating PV system. The layout of the floating PV system was designed in consideration of the optimal tilt angle and array spacing of the PV panels. The System Advisor Model (SAM) by National Renewable Energy Laboratory, USA, was used to conduct energy simulations based on weather data and the system design. The results indicated that the proposed PV system could generate 971.57 MWh/year. The economic analysis (accounting for discount rate and a 20-year operational lifetime) showed that the net present value would be $897,000 USD, and a payback period of about 12.3 years. Therefore, we could know that the economic effect of the floating PV system on the mine pit lake is relatively higher than that of PV systems in the other abandoned mines in Korea. The annual reduction of greenhouse gas emissions was analyzed and found to be 471.21 tCO₂/year, which is twice the reduction effect achieved by forest restoration of an abandoned mine site. The economic feasibility of a floating PV system on a pit lake of an abandoned mine was thus established, and may be considered an efficient reuse option for abandoned mines.

In this study, the trend of unmanned aerial vehicle (UAV) technology and its applications in the mining industry were reviewed. To understand the trend of the UAV technology, the definition and classification of the UAVs, their major applications in each field, the characteristics of the sensors of the UAV were investigated. Some examples that used the UAV technology for planning, work management, facility management, safety management, and other works in the mining industry were reviewed. By introducing the UAV technology in the mining site, it is expected that the work efficiency related to mining operations would be increased. Therefore, the application of the UAV technology in the mining industry would be expanded further.

Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.