COMBATING ILLEGAL SMALL-SCALE MINING (GALAMSEY) IN GHANA WITH ARTIFICIAL INTELLIGENCE: A COMPREHENSIVE APPROACH

Authors

  • Dennis Redeemer Korda Department of Computing & Info. Tech., Bolgatanga Technical University, Bolgatanga, Ghana
  • Nicholas Naawe Department of Mechanical Engineering, Bolgatanga Technical University, Bolgatanga, Ghana
  • Eric Ayintareba Akolgo Department of Computer Science, Regentropfen University College, Bolgatanga, Ghana
  • Obeng Owusu-Boateng Department of Mathematics/ICT, E.P College of Education, Bimbila, Ghana

Keywords:

Artificial Intelligence, Galamsey, illegal small-scale mining, Convolutional Neural Networks, Mining and drone surveillance

Abstract

Galamsey, also known as illegal small-scale mining, persists in causing significant damage to Ghana's environment, including water bodies, agricultural areas, and public health. The present study introduces a comprehensive approach that utilizes Artificial Intelligence (AI) technology to promptly identify, oversee, and forecast illicit mining operations. By employing satellite imaging, drone monitoring,
predictive analytics, and machine learning models, we propose a scalable and effective method to reduce the environmental and economic impact of galamsey. A methodology is presented that describes the development and execution of an artificial intelligence system utilizing actual data from high-risk mining areas in Ghana. The present study provides empirical evidence supporting the efficacy of artificial intelligence (AI) in facilitating real-time surveillance and forecasting forthcoming illicit mining operations. Furthermore, we address possible obstacles, including technical constraints, privacy issues, and the scarcity of proficient staff, and provide suggestions for surmounting these hindrances. 

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Published

2025-04-30