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The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.

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The technologies of the fourth industrial revolution have the potential to make zero harm possible for the first time in the history of mining. In the journey toward zero harm, rock stress monitoring systems are important for the risk management process. Although communication systems for underground mining have improved significantly over the past two decades, it remains difficult to achieve reliable-all-the-time wireless communication in ultra-deep level underground mines. The aim of this study is to explore and test a smart phone network for communicating sensor data from the underground production environment to the surface. In this paper, the evaluation and performance over distance of a wireless communication system is performed in underground mining environments. The wireless system transmits the data collected from a sensor installed in a narrow reef stope, horizontal tunnel, and vertical shaft area of a mock underground mine. The evaluation was performed using the received signal strength of a mobile receiver over distance. The path loss coefficients of the underground mining environment were then derived for the measurement areas. The results show that a communication speed of 80 Mbps was achieved in a 60 m range, thus, indicating the potential for the support of applications requiring higher data rates.

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In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images.