Document Type : Review Paper

Authors

• Ukpata Austin Odo ¹
• Jude S Ejepu ²
• Bernd Striewski ¹

¹ Geological Survey of Western Australia, 100 Plain Street, East Perth WA, 6004, Western Australia, Australia

² Federal University of Technology, Minna, P.M.B. 65, Minna, Niger State, Nigeria

Abstract

The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), remote sensing (RS), LiDAR, drone mapping, and positioning systems find applications in mineral exploration, mine planning, operational monitoring, and post-mining rehabilitation. Artificial intelligence (AI) and machine learning (ML) systems enhance the functional potential of these technologies through predictive modeling capabilities, which work in conjunction with real-time analytic functions. The research shows that these technologies enable better decision-making, performance optimisation, and environmental risk reduction. Modern mining relies entirely on these technologies because they support accurate resource assessment, optimise design operations, and help enforce safety standards and environmental codes. Adopting such technologies requires resolving implementation costs, addressing data integration issues, and acquiring the necessary technical expertise. The future development of mining technology should focus on enhancing the integration of geospatial information platforms, creating sustainable solutions for medium-sized mining operations at affordable prices, and developing predictive evaluation systems utilizing AI algorithms. The mining industry accomplishes safer operation methods through efficient technologies, enhancing sustainability.

Keywords

• Geospatial technologies
• mining industry
• remote sensing
• artificial intelligence
• environmental monitoring

Main Subjects

• Data Mining