Multi-source and multi-scale earth observation and novel machine learning methods for mineral exploration and mine site monitoring

Project: EU project

Project Details

Description

The Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring (MultiMiner) is a pan-European consortium consisting of 12 partners and an associated partner from research institutes (7), academia (2), consulting businesses (1) and mining industry (3) with interdisciplinary backgrounds in geology, remote sensing and machine learning. The consortium members come from six EU member states (FI, DE, FR, CZ, AT, EL) which represent mining regions across Europe with diverse geology with evident potential for various types of CRM resources and thousands of operational and closed mines.

MultiMiner develops novel data processing algorithms for cost-effective utilization of Earth Observation (EO) technologies for mineral exploration and mine site monitoring. MultiMiner unlocks the potential of EO data, including Copernicus, commercial satellites, upcoming missions, airborne and low altitude as well as in situ data, to support the entire mining life cycle including mineral exploration, operational, closure and post-closure stages. This is achieved by creating generic but highly innovative machine learning solutions which do not require any or only little ground truth data. The project focuses on new EO based exploration technologies for critical raw materials (CRM) to increase the probability of finding new sources within EU thereby strengthening the EU autonomy in the area of raw materials. MultiMiner EO based exploration solutions have extremely low environmental impact, and are thus socially acceptable, economically efficient and improve safety. The project’s solutions for mine site monitoring increase the transparency of mining operations as environmental impacts can be detected as early as possible and digital information of the currently unexploitable raw materials can be stored for future generations. The applicability of the developed algorithms is demonstrated in 4 European test sites.

Grant Agreement No. 101091374-HORIZON-CL4-2022-RESILIENCE-01-08MULTIMINER
AcronymMultiMiner
StatusActive
Effective start/end date1/01/2330/06/26

Collaborative partners

  • VTT Technical Research Centre of Finland
  • Geological Survey of Finland (GTK) (Project partner) (lead)
  • Institute of Geology & Mineral Exploration (EAGME) (Project partner)
  • Yara Suomi Oy (Project partner)
  • University of Leoben (Project partner)
  • EFTAS Fernerkundung Technologietransfer GmbH (Project partner)
  • Veitsch-Radex GmbH & Co OG (Project partner)
  • European Science Foundation (ESF) (Project partner)
  • Institute for Geosciences and Natural Resources (BGR) (Project partner)
  • Hellas Gold S.A. (Project partner)
  • Czech Geological Survey (Project partner)
  • Technical University of Munich (TUM) (Project partner)
  • GeoSphere Austria (Project partner)

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 2 - Zero Hunger
  • SDG 6 - Clean Water and Sanitation
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 12 - Responsible Consumption and Production

Funding category

  • Horizon Europe

Keywords

  • HORIZON-CL4-2022-RESILIENCE-01-08
  • remote sensing
  • machine learning
  • geology
  • data processing

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