Results

Acid mine drainage (AMD) environments host complex mineral–water systems that are not well represented by spectral libraries derived solely from pure minerals. We present an open dataset that in…

Acid mine drainage (AMD) environments host complex mineral–water systems that are not well represented by spectral libraries derived solely from pure minerals. We present an open dataset that integrates spectroscopy of natural AMD materials with co-located mineralogical and geochemical measurements from the Kirki (Saint Philippos) mine in NE Greece. The dataset includes: (i) laboratory mineral reflectance spectra spanning 350–15,375 nm (visible–near infrared (VNIR)–shortwave infrared (SWIR)–mid-wave infrared (MWIR)–longwave infrared (LWIR)) for 26 compositionally diverse solid samples; (ii) in situ water VNIR spectra from 337–823 nm collected over AMD-impacted streams and lakes; (iii) co-located field measurements (e.g., temperature, pH, electrical conductivity, turbidity); and (iv) an extensive suite of laboratory analyses for both solids and waters, including mineralogical, major-element, and trace-element data. All spectral and analytical products are provided in a harmonized format, with detailed metadata, instrument specifications, and processing documentation. The dataset is designed to support the development and testing of spectral analysis methods, quantitative retrieval algorithms, and cross-sensor comparisons in AMD settings, and to serve as a high-fidelity analog resource for planetary spectroscopy where sulfate- and Fe-oxide detection and longwave infrared mineral characterization are of interest.

Critical raw materials (CRMs) are central to Europe’s economy and its transition to a sustainable and digital future. They are essential for the development of advanced technologies for clean en…

Critical raw materials (CRMs) are central to Europe’s economy and its transition to a sustainable and digital future. They are essential for the development of advanced technologies for clean energy and digital infrastructures. For instance, lithium, cobalt and nickel are used in batteries for electric vehicles, Rare Earth Elements (REE) are critical for permanent magnets in wind turbines and electronics, and copper, silicon and tantalum are required for artificial intelligence (AI) infrastructures. Demand for these materials is predicted to increase manifold over the next decades, while recent crises such as the COVID-19 pandemic and geopolitical tensions have shown how fragile and import-dependent Europe’s supply chains remain. Securing reliable access to CRMs is now a strategic priority, and the European Commission has introduced the European Critical Raw Materials Act (CRMA), as part of its Green Deal Industrial Plan. The CRMA sets ambitious goals: scale up domestic production, diversify sourcing, increase circularity (recycling and substitution), and strengthen permitting, monitoring and innovation. Achieving these objectives requires not only funding and regulatory reform but also robust, operational technologies capable of improving CRM exploration, mining operations, environmental monitoring, traceability and regulatory compliance across the full CRM lifecycle. Earth Observation (EO) and Machine Learning (ML) feature among the most promising fields to address these objectives. EO provides a continuous flow of information on geology, the environment, land use and ground stability while ML helps turning complex datasets into practical insights. Together, they can reduce costs and risks in mineral exploration, increase the sustainability and efficiency of mining operations, improve environmental monitoring, and speed up decision-making. Several Horizon Europe projects, joining forces within the European Sustainable Mining and Innovation Network (ESMIN), are developing and testing such innovative EO and ML solutions. This policy brief presents preliminary results and illustrates how EO and ML can support mining companies, geological surveys, non-governmental organisations and national institutions in delivering on the CRMA objectives.Written by MultiMiner, GoldenRAM, MOSMIN, AGEMERA, M4Mining, and MINEYE as representatives of ESMIN (European Sustainable Mining & Innovation Network).  MultiMiner was funded by the European Union’s HORIZON research and innovation actions programme under Grant Agreement No. 101091374. GoldenRAM was funded by the European Union’s HORIZON research and innovation actions programme under Grant Agreement No. 101138153. AGEMERA was funded by the European Union’s HORIZON research and innovation actions programme under Grant Agreement No. 101058178. MOSMIN is funded by the European Union Agency for the Space Programme and the European Union‘s Horizon Europe innovation action programme under grant agreement No 101131740. M4Mining was funded by the European Union’s HORIZON research and innovation actions programme under Grant Agreement No. 101091462 MINEYE was funded by the European Union’s HORIZON research and innovation actions programme under Grant Agreement No. 101138456.

Acid mine drainage (AMD) environments host complex mineral-water systems that challenge conventional spectral libraries built for pure minerals. We present an open, harmonized dataset that integrates …

Acid mine drainage (AMD) environments host complex mineral-water systems that challenge conventional spectral libraries built for pure minerals. We present an open, harmonized dataset that integrates spectroscopy of real AMD materials with co-registered mineralogical and geochemical measurements from the Kirki (Saint Philippos) mine, NE Greece. The dataset comprises: (i) laboratory mineral reflectance spectra spanning 350–15,375 nm (Visible–Near Infrared (VNIR) – Shortwave Infrared (SWIR) – Mid-Wave Infrared (MWIR) – Longwave Infrared (LWIR) for natural, compositionally diverse complex mineral samples; (ii) in situ water VNIR spectra from 337–823 nm acquired across a gradient of acidity and turbidity; (iii) co-located field measurements (e.g. temperature, pH); and (iv) wide range of laboratory analyses for both solid and water datasets to support ground truth validation. To demonstrate utility, we relate spectral features to mineralogy and composition using partial least squares regression (PLSR). For solids, VNIR–SWIR regions best predict Fe2O3, while MWIR–LWIR features improve estimation of SiO2 and total S, highlighting diagnostic bands across iron oxides/sulfates and silicate frameworks. For waters, VNIR spectra capture acidity/turbidity-driven variability and enable example detecting gradients of dissolved trace metals. Together, these results show that realistic mixture spectra coupled to independent ground truth support quantitative inference of key AMD parameters. These free datasets can facilitate algorithm development, cross-sensor validation, and environmental assessment in AMD-impacted settings, and serves as a high-fidelity analog for planetary spectroscopy, particularly for sulfate- and Fe-oxide detection and LWIR mineral retrievals. All data products, metadata, and processing notes are openly available via the referenced repository.  The associated data paper is available at: https://www.nature.com/articles/s41597-026-07307-y - citeas The MultiMiner project is funded by the European Union’s Horizon Europe research and innovations actions programme under Grant Agreement No. 101091374.

The Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring (MultiMiner) project develops novel data processing algorith…

The Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring (MultiMiner) project develops novel data processing algorithms for cost-effective utilization of Earth Observation (EO) technologies for mineral exploration and mine site monitoring. 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 EUautonomy in the area of raw materials. In this document, the field work and laboratory work protocols of the MultiMiner project are described. This relates to the actual materials and parameters measured and includes protocols for geological mapping, geochemical analysis, vegetation mapping, water analyses, spectral analyses, ground moisture measurements, dust monitoring, as well as dam stability and open pit stability monitoring. In terms of laboratory measurements spectral data acquisition protocols, X-Ray Fluorescence (XRF), X-RayDiffraction (XRD), inductively coupled plasma mass spectrometry (ICP-MS), petrographic description of rock samples in thin section, scanning electron microscope-energy dispersive X-ray spectroscopy (SEMEDS) and electron probe microanalysis (EMPA) are described. Furthermore, Appendix 1, describes the structure of the metadata database of the project. The regional geology of the Hochfilzen test site and the Kallyntiri test site are presented in Appendices 2 and 3, respectively. In Appendices 4 and 5, the hyperspectral field and laboratory protocols are documented.

The Multi-source and Multi-scale Earth observation and Novel Machine LearningMethods for Mineral Exploration and Mine Site Monitoring (MultiMiner) projectis a Research and Innovation Action (RIA) proj…

The Multi-source and Multi-scale Earth observation and Novel Machine LearningMethods for Mineral Exploration and Mine Site Monitoring (MultiMiner) projectis a Research and Innovation Action (RIA) project funded from the Horizon Europeprogramme in years 2023–2036. The goal of the project is to develop novel dataprocessing algorithms for cost-effective utilisation of Earth Observation (EO) technologiesfor mineral exploration and mine site monitoring (Fig. 1). The purposeis to further unlock the potential of EO data, including Copernicus, commercialsatellites, upcoming missions, airborne and low altitude as well as in situ data.Solutions are created for all stages of the mining life cycle including mineralexploration, operational, closure and post-closure stages. The core idea of theMultiMiner project is to create innovative machine learning solutions which donot require any or only little in situ reference data. The project focuses on newEO based exploration technologies for critical raw materials (CRM) to increasethe probability of finding new sources within EU thereby strengthening the EUautonomy in the area of raw materials. MultiMiner EO based exploration solutionshave extremely low environmental impact, and are thus socially acceptable,economically efficient and improve safety. The project’s solutions for mine sitemonitoring increase the transparency of mining operations as environmentalimpacts can be detected as early as possible and digital information of the currentlyunexploitable raw materials can be stored for future generations. The applicabilityof the developed algorithms is demonstrated in five European test sites (Fig. 2).The core scientific work in the project is divided into three work packages wherethe first two develop algorithms for mineral exploration and mine site monitoring,and the third one demonstrates their applicability and accuracy in real mining andexploration environments of critical and strategic raw materials.