Illegal small-scale mining is a growing industry in many developing countries. In these mines, gold and other precious minerals are extracted in a low-tech, labor-intensive process. While these mines provide huge employment and income potential for poverty-stricken communities, they are also linked to environmental damages, health hazards, and social ills. However, since these small mining operations are mostly illegal, there is virtually no data to analyze their exact impact.
Michael Lanzetta and Elena Terenzi offer an overview of a collaboration between Microsoft and the Royal Holloway University, London, that applies deep learning to locate illegal small-scale mines in Ghana using satellite imagery and investigates their impact on surrounding populations and environment. The goal of the project is to enable better-informed policy decisions by relevant stakeholders. First, the team built an image classification model in Keras and scaled the training of the model using Kubernetes on Azure. Once the mines were identified, the team investigated the impact of those mines on surrounding environments and populations in Python.
Elena Terenzi is a software development engineer at Microsoft, where she brings business intelligence solutions to Microsoft Enterprise customers and advocates for business analytics and big data solutions for the manufacturing sector in Western Europe, such as helping big automotive customers implement telemetry analytics solutions with IoT flavor in their enterprises. She started her career with data as a database administrator and data analyst for an investment bank in Italy. Elena holds a master’s degree in AI and NLP from the University of Illinois at Chicago.
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