Periodic Reporting for period 2 - MultiMiner (MULTI-SOURCE AND MULTI-SCALE EARTH OBSERVATION AND NOVEL MACHINE LEARNING METHODS FOR MINERAL EXPLORATION AND MINE SITE MONITORING)
Berichtszeitraum: 2024-07-01 bis 2025-06-30
European Union aims to become a carbon-neutral economy by 2050. To achieve this industry transformation is required in the raw materials sector to supply critical (CRM) and strategic raw materials for the European market. Thus, the EU Action Plan for CRM 2020 and the CRM Act of 2023 were formulated to list concrete actions aimed to mitigate supply risks, ensure environmental protection, and the EU’s strategic autonomy in the raw materials market. In addition, the European Green Deal and the Circular Economy Action Plan and a number of international, EU and state level regulations provide frameworks to support integration of sustainability and circularity principles across the raw materials sector. The resilient supply of raw materials also requires exploration of previously undiscovered mineralization within EU but mineral exploration should be conducted preserving the environment. Thus, traditional CRM exploration methods with low environmental impacts should be complemented with new advanced methods. Additionally, the extraction of raw materials should be conducted sustainably to align with the above mentioned regulatory and policy frameworks.
Remote sensing provides a set of low-impact tools to support the entire mining life cycle including mineral exploration, operational, closure and post-closure stages. The use of remote sensing in its full potential has been hampered by lack of understanding of the method applicability in mining industry, need for easy-to-use data processing tools and availability of suitable in situ data. The goal of the project Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring (MultiMiner) is to develop novel data processing algorithms for efficient utilization of Earth Observation (EO) technologies for mineral exploration and mine site monitoring. MultiMiner unlocks the potential of EO data, including Copernicus, commercial satellites, airborne, drone and in situ data, to support the entire mining life cycle. The general objectives of MultiMiner are to 1) develop scalable and automated approaches for mineral exploration based on multi-source EO data and sparse in situ data, focused on mineral deposits hosting CRMs across EU, and to 2) leverage novel EO data analysis methods to make the most of scarcely available in situ data for timely mine site monitoring, reducing both disruptions to mining activities and environmental impacts.
The goals are achieved by creating generic but innovative unsupervised and weakly supervised Machine Learning (ML) approaches and tools for multi-scale image processing which augment and potentially substitute in situ data collected on the ground. For exploration purposes, we will develop of a novel mineral mapping algorithm performing automatic spectral feature extraction from a comprehensive mineral spectral library. In addition, a mineral prospectivity wizard facilitating multi-scale mineral mapping and automatic interpretation for non-experts will be constructed.
For environmental monitoring of mining activities, we will develop EO solutions for:
- 3D/4D monitoring of composition and volume of tailings storage facilities
- automatic interpretation of European Ground Motion Service products, producing maps for dam stability and open pit stability
- algorithms and maps of ground moisture, supporting dam stability as well
- monitoring algorithms for water quality and acid mine drainage
- algorithms and maps of revegetation status, including vegetation structure and biodiversity
- multi-source models for atmospheric and surface dust monitoring.
Overarching these thematic applications, a Generic Mine Site Monitoring algorithm (GMSM) will be developed. GMSM uses advanced deep learning methods and makes the most of the vast amounts of available EO data and sparser in situ data in respective applications.
The project focuses on new EO based exploration technologies for 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 are fast, economically efficient and have an extremely low environmental impact. They improve safety and are environmentally safe, making them potentially more socially accepted. 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. MultiMiner aims to create new, innovative tools utilizing EO data that can be used to discover additional primary resources within Europe, thus ensuring a legacy of EU raw material self-sufficiency.
Remote sensing provides a set of low-impact tools to support the entire mining life cycle including mineral exploration, operational, closure and post-closure stages. The use of remote sensing in its full potential has been hampered by lack of understanding of the method applicability in mining industry, need for easy-to-use data processing tools and availability of suitable in situ data. The goal of the project Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring (MultiMiner) is to develop novel data processing algorithms for efficient utilization of Earth Observation (EO) technologies for mineral exploration and mine site monitoring. MultiMiner unlocks the potential of EO data, including Copernicus, commercial satellites, airborne, drone and in situ data, to support the entire mining life cycle. The general objectives of MultiMiner are to 1) develop scalable and automated approaches for mineral exploration based on multi-source EO data and sparse in situ data, focused on mineral deposits hosting CRMs across EU, and to 2) leverage novel EO data analysis methods to make the most of scarcely available in situ data for timely mine site monitoring, reducing both disruptions to mining activities and environmental impacts.
The goals are achieved by creating generic but innovative unsupervised and weakly supervised Machine Learning (ML) approaches and tools for multi-scale image processing which augment and potentially substitute in situ data collected on the ground. For exploration purposes, we will develop of a novel mineral mapping algorithm performing automatic spectral feature extraction from a comprehensive mineral spectral library. In addition, a mineral prospectivity wizard facilitating multi-scale mineral mapping and automatic interpretation for non-experts will be constructed.
For environmental monitoring of mining activities, we will develop EO solutions for:
- 3D/4D monitoring of composition and volume of tailings storage facilities
- automatic interpretation of European Ground Motion Service products, producing maps for dam stability and open pit stability
- algorithms and maps of ground moisture, supporting dam stability as well
- monitoring algorithms for water quality and acid mine drainage
- algorithms and maps of revegetation status, including vegetation structure and biodiversity
- multi-source models for atmospheric and surface dust monitoring.
Overarching these thematic applications, a Generic Mine Site Monitoring algorithm (GMSM) will be developed. GMSM uses advanced deep learning methods and makes the most of the vast amounts of available EO data and sparser in situ data in respective applications.
The project focuses on new EO based exploration technologies for 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 are fast, economically efficient and have an extremely low environmental impact. They improve safety and are environmentally safe, making them potentially more socially accepted. 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. MultiMiner aims to create new, innovative tools utilizing EO data that can be used to discover additional primary resources within Europe, thus ensuring a legacy of EU raw material self-sufficiency.
The main achievements of WP1, Project management, was the organization of two on-line project progress meetings: 10/2024 and 3-4/2025. Coordination of project activities and reporting have been proceeded as planned. MultiMiner has produced two internal progress reports and three deliverables during RP2.
In WP2, Scalable Mineral Prospectivity Tools, the final version of the Mineral Mapping Algorithm (MMA) was developed. Training data and strategy for AI driven data analysis is implemented and currently the AI algorithm has started to be trained to build a foundation model. The MMA was applied to UAV and satellite borne HSI data. First mineral maps have been generated from Hochfilzen and data analyses has started for the Greek test sites. The development of the Mineral Prospectivity Wizard has continued. For automatic data interpretation, functionality to read and use GIS information was included. Acquisition and preprocessing of main HSI data from all test sites are finished, and an additional airborne HSI flight is planned for summer 2025 in Hochfilzen. Analyses and automatic interpretation of data has started.
In WP3, Timely Mine Site Monitoring Methods, a first prototype of GMSM algorithm was developed, as a minimal version addressing two common EO data combinations (Sentinel-2 time series and EnMAP). In all thematic applications all available free EO data has been downloaded, pre-processed and partially analysed for all sites; in situ reference data collected at all sites was combined with EO data; results of baseline methods are progressing towards finalization. The GMSM was applied to the vegetation task and is currently being adapted to water quality task and others.
In WP4, Site Demonstrations, data uploads continued into the metadata database and the ability to export metadata in an ISO 19115 -format was implemented. A step-by-step standard operating procedure was written to describe how data and metadata are shared in the project. Preparations for the accuracy assessment report of the novel products were actively, and preliminary accuracy assessment was conducted in WPs 2 and 3. Field work continued in Hochfilzen (Austria), Ihalainen (Finland) and Kallyntiri and Chalkidiki (Greece). Laboratory work (X-Ray Diffraction, X-Ray Fluorescence XRF, micro-XRF, FE-Field Emission Scanning Electron Microscope, Laser Ablation - Inductively Coupled Plasma Mass Spectrometry/Optical Emission Spectrometry, Electron Probe Microanalysis, thin section analysis and hyperspectral measurements) were carried out on samples collected during previous field campaigns.
In WP2, Scalable Mineral Prospectivity Tools, the final version of the Mineral Mapping Algorithm (MMA) was developed. Training data and strategy for AI driven data analysis is implemented and currently the AI algorithm has started to be trained to build a foundation model. The MMA was applied to UAV and satellite borne HSI data. First mineral maps have been generated from Hochfilzen and data analyses has started for the Greek test sites. The development of the Mineral Prospectivity Wizard has continued. For automatic data interpretation, functionality to read and use GIS information was included. Acquisition and preprocessing of main HSI data from all test sites are finished, and an additional airborne HSI flight is planned for summer 2025 in Hochfilzen. Analyses and automatic interpretation of data has started.
In WP3, Timely Mine Site Monitoring Methods, a first prototype of GMSM algorithm was developed, as a minimal version addressing two common EO data combinations (Sentinel-2 time series and EnMAP). In all thematic applications all available free EO data has been downloaded, pre-processed and partially analysed for all sites; in situ reference data collected at all sites was combined with EO data; results of baseline methods are progressing towards finalization. The GMSM was applied to the vegetation task and is currently being adapted to water quality task and others.
In WP4, Site Demonstrations, data uploads continued into the metadata database and the ability to export metadata in an ISO 19115 -format was implemented. A step-by-step standard operating procedure was written to describe how data and metadata are shared in the project. Preparations for the accuracy assessment report of the novel products were actively, and preliminary accuracy assessment was conducted in WPs 2 and 3. Field work continued in Hochfilzen (Austria), Ihalainen (Finland) and Kallyntiri and Chalkidiki (Greece). Laboratory work (X-Ray Diffraction, X-Ray Fluorescence XRF, micro-XRF, FE-Field Emission Scanning Electron Microscope, Laser Ablation - Inductively Coupled Plasma Mass Spectrometry/Optical Emission Spectrometry, Electron Probe Microanalysis, thin section analysis and hyperspectral measurements) were carried out on samples collected during previous field campaigns.
The work is proceeding in schedule to reach the exploitable results.