Illegal mining continues to damage Ghana’s farmlands, forests, and rivers, leaving long-term environmental and economic consequences. As they researched this national challenge, Computer Science majors Vera Anthonio ’26 and McNobert Amoah ’26 uncovered a critical gap: most detection methods still rely on manual reporting. In many cases, by the time an incident is documented, the environmental harm is already substantial. This delay made it clear that earlier, technology-driven detection could meaningfully support intervention efforts.

Motivated by this insight, the team began exploring how to combine satellite imagery, computer vision, and machine learning to identify patterns associated with illegal mining. Their project focuses on building a system capable of analysing satellite data, flagging high-risk areas, and generating geolocated reports that can guide environmental agencies to quicker, more informed responses. “What excites me most is seeing how powerful technology becomes when it’s applied to real-world problems,” Anthonio shared.