Detecting Illegal Small Scale Mines in Ghana using Deep Learning

Olivia Klose Computer Vision enables computers to obtain a
high-level understanding from images and videos by automatically
extracting, analysing and understanding useful information. With
autonomous driving, visual failure detection or scene
understanding, computer vision is becoming one of the focus areas
in artificial intelligence (AI) to enable computers to see and
perceive like humans. In this talk we will present our ongoing
collaboration with the Royal Holloway - University of London on
illegal small scale mines in Ghana. Illegal small-scale mining is a
growing industry in many African, Asian and Latin American
developing countries. Gold and other precious minerals are
extracted in a low-tech, labour-intensive process linked to
environmental damages, health hazards and social ills.
Additionally, the process provides huge employment and income
potential in poverty-stricken communities. Since these small mining
operations are mostly illegal, there is virtually no data to
analyse their exact impact. This project seeks to fill this void to
enable better-informed policy decisions by relevant stakeholders.
We built an image classification model in Keras and scaled the
training of the model using Kubernetes on Azure. Once small scale
mines were identified, we investigated the impact of those mines on
surrounding environments and populations in Python. supported by
BMZ