EMERGING TECHNOLOGIES IN MINERAL EXPLORATION: A REVIEW OF ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND REMOTE SENSING APPLICATIONS IN AFRICA'S MINING SECTOR

Abstract

The global energy transition is driving unprecedented demand for critical minerals, placing Africa, which holds over 30% of the world's mineral reserves, at the center of the new energy economy. Despite this immense geological potential, the continent attracts less than 5% of global exploration expenditure, a disconnect stemming from systemic non-technological barriers rather than a lack of resources. This paper provides a comprehensive review of how emerging technologies including Artificial Intelligence (AI), Machine Learning (ML), and Remote Sensing are being applied to transform mineral exploration in Africa. The study examines the foundational principles and practical applications of these technologies, drawing on specific case studies such as the use of AI to enable the discovery of immense copper deposits in Zambia and the application of satellite imagery for gold prospecting in Mauritania. However, the analysis also critically evaluates the primary non-technological barriers that impede their widespread adoption, including regulatory inefficiencies, infrastructural deficits, and a persistent skills gap. The findings conclude that while these technologies offer a powerful pathway to de-risking exploration and improving efficiency, their full benefits can only be realized when supported by fundamental reforms in governance, policy, and human capital development. This integrated approach is essential for aligning technological innovation with sustainable and equitable development in Africa's mining sector