Five mining sites in Germany, Finland, Romania, Bulgaria, and Kosovo are to pilot the GoldenEye platform.
GoldenEye entails developing an analytics platform for monitoring and analysing mine sites across the EU to improve their productivity and worker safety and reduce the environmental impact of extraction. Under the terms of the three-year programme, which has a budget of €8.3mn, EOS Data Analytics is providing satellite data processing.
The platform, powered by machine learning algorithms, combines earth observation technologies with on-site sensing: It processes data from satellites, drones, and ground-based sensors and extracts actionable intelligence about mining sites. That way, the AI platform supports decision-making at all stages of the mine’s life cycle, from exploration to extraction and post-closure.
Within the GoldenEye EU H2020 project for monitoring and analysing mine sites across the EU, EOS Data Analytics, a global provider of AI-powered satellite imagery analytics, gathered ground-based data at a copper ore quarry in Bulgaria to validate the satellite monitoring technology for the project’s AI platform.
Specialists are in charge of ecological monitoring on numerous indicators, such as the assessment of slope stability, allocation of areas of active extraction, humidity and surface temperature, and vegetation condition of adjacent territories.
The project’s goal is to improve the mine sites’ productivity and worker safety and reduce the environmental impact of extraction.
“EOSDA uses various image processing approaches, including machine learning and deep learning algorithms," said Dr. Nataliia Borotkanych, Projects Coordinator at EOS Data Analytics.
"Not all methods work the same around the world and with all types of minerals. Therefore, we can’t go without an area’s visual overview and ground data collection. We need them to calibrate and validate results obtained from satellites."
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