MINEYE - EARTH OBSERVATION TECHNIQUES FOR MINE LIFE CYCLE MONITORING USING ML-BASED DATA FUSION APPROACH

The MINEYE (Earth Observation techniques for MINe lifE cYclE monitoring using ML-based data fusion approaches) consortium consists of 13 partners from nine European countries and aims to strengthen EU autonomy in primary and secondary raw materials, at the same time as reducing environmental impact. This is done by development of innovative cost-effective, time-efficient and competitive Earth Observation (EO) based methodologies and technologies for the entire mining value chain. MINEYE will integrate EO data with other data sources such as EuroGeoSource, ProMine, EGDI and will collect new EO, remote sensing, geophysical and in-situ data. MINEYE aims at developing novel data fusion and processing algorithms for EO technologies for the mineral exploration, operational, closure and post-closure stages. MINEYE unlocks the potential of EO data, including Copernicus, commercial satellites, upcoming hyperspectral missions, airborne as well as in situ data. This is achieved by creating generic innovative machine learning algorithms tailored to solve specific needs within the mining value chain. MINEYE’s overall objective and ambition is to increase the access to critical and strategic minerals and metals in Europe with full consideration of environmental and social aspects, by providing the mining industry with proven and validated solutions for uptake of EO data and technology. MINEYE's final product is a web-interface, IPOP (Interfacing, Programming and Optimisation Platform) for providing data and result. IPOP has three outputs each covering a specific part of the mining value chain: (1) methods for Mineral Prospectivity Mapping (2) EO plugin for LiquidEarth software for 3D modelling, (3) evaluation and maps of re-mining potential and tailings characterization. In addition, ground stability assessment and environmental monitoring are included to support the development of a socially responsible mining. IPOP further serves as a multi-criteria decision-making tool.