The Goldeneye, i.e. ‘Earth observation (EO) and Earth global navigation satellite system (GNSS) data acquisition and processing platform for safe, sustainable and cost-efficient mining operations’, project implements a unique combination of remote sensing and positioning technologies, exploiting EO and Earth GNSS data, together with data fusion and processing powered by data analytics and machine-learning algorithms. The aim of this paper is to evaluate the impact provided by seven techniques and methods for the five mine sites in the Goldeneye project. The emphasis is to analyse and reflect how, and with what focus areas, the developed analytical capabilities meet the needs of different mine sites that collaborated in the project in piloting and testing. The results show that the outcomes of this research and development project hold immense significance for the mining industry, as they contribute to its transformation, digitalization and twin transition. This contribution is realized through the broad adoption of sensing and artificial-intelligence-based applications, solutions and services within the mining industry. This benefits not only the companies directly involved but also the entire value chain and related industries, and contributes to the European Union level and national goals in the mining sector.

Earth observation technologies for mining are widely available and currently transitioning from specialized, experimental tools to essential operational capabilities for exploration, safety and environmental monitoring. The market is driven by high-resolution imagery, synthetic aperture radar that works through cloud cover and artificial intelligence (AI)-driven analytics, which allow for real-time monitoring of mine sites and remote exploration. Together with recent advancements in unmanned aerial vehicle capabilities, proximity sensing and positioning technologies, these systems enable accurate multiscale applications from exploration to environmental monitoring in the mining environments, infrastructures, and site-specific operations and processes. This book presents remote sensing applications as well as proximity and positioning technologies utilizing advanced data fusion, and it also introduces the Goldeneye platform that employs AI, including natural language processing to analytical and visualization tools simplifying computational tasks. Demonstrated in five field trials across Germany, Bulgaria, Romania, Kosovo and Finland, the results show potential for better environmental monitoring as well as for time and cost savings in exploration, mine safety monitoring and operations reporting. Overall, the technologies and their adaptations contribute to a more responsible and sustainable supply of raw materials within Europe.

Fuzzy logic-based mineral potential mapping was applied to the Tisov & aacute;-Klingenthal Cu-Co VMS deposit (Erzgebirge) in the Czech-German border region. The study area is characterized by heterogeneous geological and geochemical datasets derived from differing national surveys and historical mining. Using the Exploration Information System (EIS) toolkit, a knowledge-driven fuzzy logic approach integrated key spatial datasets, including copper and zinc soil and stream sediment anomalies and metabasalt lithology, relevant to Besshi-type VMS deposits. Three prospective anomalies were identified: the historic Tisov & aacute; mine and two additional targets aligned along the same stratigraphic horizon. Artificial Neural Network (ANN) modelling was limited by insufficient training data, resulting in overfitting and reduced predictive reliability. Follow-up soil geochemical surveys conducted over the largest anomaly returned locally elevated copper values but did not conclusively confirm mineralisation. The results demonstrate that fuzzy logic provides a flexible and interpretable framework for mineral potential mapping in complex, data-scarce environments and highlight the need for iterative modelling and targeted exploration.

To advance Europe's self-reliance in sustainably sourcing lithium, innovative exploration technologies that enhance the efficiency of both exploration and extraction are urgently required. Lithium's role in rechargeable batteries necessitates advanced methods to support domestic resource development and meet rising demand. This study evaluates artificial neural networks (ANN) combined with hyperspectral data for lithium prediction, comparing the HySpex Mjolnir S-620 sensor and the Technical Research Centre of Finland Oy's (VTT) novel active hyperspectral scanner (AHS). The AHS employs a broadband short-wave infrared supercontinuum light source, avoiding dependency on external illumination and overcoming limitations of traditional systems in underground settings. At Germany's Zinnwald/Cínovec greisen deposit, lithium prediction was achieved via ANN trained on portable X-ray fluorescence (pXRF)-derived rubidium data, a proxy for lithium in zinnwaldite-rich mineralization. Hyperspectral processing extracted diagnostic absorption features (e.g. band ratios, minimum wavelength mapping) and classified lithium-bearing minerals. The ANN model produced a high-resolution lithium concentration map, demonstrating the AHS's ability to enhance mine-face characterization for both detailed exploration and operational workflows, such as grade control. Integrating hyperspectral sensing and machine learning supports Europe's sustainable objectives by enabling more precise and efficient resource extraction, offering scalable solutions for modern mining. The AHS–ANN framework also holds potential for agriculture, environmental monitoring and defence, where rapid material characterization is paramount.

<p>The Goldeneye project combines remote sensing and positioning technologies with proximal sensing to produce reference calibrated mineralogical maps through data fusion. The project brings together optical satellite sensor data, drone sensor aerial data both optical and electromagnetic as well as spectral ground sensor data. Satellite data can offer spectral signatures of large areas but suffers from limited spatial resolution and blind spots where the higher resolution satellite data is not available. Drone data can offer more variety in spectral wavelengths with higher resolution but there are some drawbacks as well. Namely, NIR vibrational spectroscopy requires background information for successful mineralogical analysis. In addition, the most interesting SWIR range is challenging due to large and very expensive sensors. To cope with these challenges of aerial data, proximal sensing can be applied in locations where satellite imagery is not available, and it can also produce reference information for the calibration of the spaceborne and airborne instruments. The conventionally used analyses for producing mineralogical information from field collected samples are the mineral liberation analysis (MLA) and X-Ray diffraction (XRD) which require extensive sample preparations and are laborious and slow. Goldeneye-project has studied the use of time-gated Raman for easier and more practical production of reference data at the field sites as well as from field collected rock samples. The benefit of Raman is an accurate characteristic spectral fingerprint and an ability to distinguish small mineralogical features as the detection spot is in the range of hundreds of microns. There are continuous wave Raman spectrometers, which are already field deployable. However, conventional Raman suffers from the auto-fluorescence emission triggered by the laser illumination, especially in light-colored rock samples. Time-gated (TG) Raman has the benefit of time-resolved sensing, where the Raman scattering is recorded before the fluorescence signal is over-powering the weaker scattered signal. TG-Raman can thus offer information from a wider variety of geological specimen than the conventional Raman. In Goldeneye-project, TG-Raman spectra were collected with custom sampling solution from mineral samples and drill cores from Erzgebirge exploration site in Germany. The data was analysed together with pXRF reference data to assess the benefit of the data fusion.</p> <p>The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.</p>

From the middle of the 17th to the beginning of the 19th century, tin mining was carried out near Bockau and Aue in the Westerzgebirge. The object of the mining was stratiform mineralization. Near Bockau, (underground) tin mining was first mentioned in documents in 1663 and was active, with interruptions, until the beginning of the 19th century. On an area of more than 2 km², hundreds of dumps and pits, numerous abandoned mines, and historically very remarkable underground objects are known. Mining was preferably carried out near the surface. Despite favourable morphological conditions, hardly any deep adits were built and depths of more than 20 m were rarely reached. In this context, Pingen are abandoned ore pits or prospecting sites, where ores and other mineral resources were mined. Geometrically Pingen resemble round depressions created by the collapse of a mine workings (shaft, adit, underground drift), collapsed due to its age, leaving this relic usually funnel-shaped (down-facing cone), often surrounded by an annular dump (0.5 to 3 m) caused by the lowered surface.The main aim of this application is to identify mining relics (Pingen) using a UAV equipped with LiDAR technology. The LiDAR technology allows to obtain a high-resolution Digital Elevation Model (DEM) and Point cloud of the surveyed area. The DEM is the digital representation of topographic and manmade features located on the surface of the earth.In this framework, a LiDAR survey was conducted in a flight area of about 1,6 km² within the “Bockau” area during August 2022. The surveyed features include the elevations, colorized Point Cloud (RGB values), transparency levels, reflectance values, and number of returns (significant for the penetration of the vegetation). This information was used to derive the final products: DEM and classified Point cloud. Various spatial analyses were conducted and tested on the DEM and Point Cloud to automatically identify the mining relics. Hydrogeological analysis showed to be the best approach for the automatic identification of Pingen. As a result, the ground depressions were identified and nested surfaces were delineated.The automatically identified features were validated by examination of randomly selected samples on the surveyed point cloud, comparison to identified features based on the National dataset of the LiDAR Database and field verification. The validation revealed, that around 90% of the Pingen in the study area were successfully identified with the developed workflow. Other features of interests couldn’t be identified due to the similarity of geometric properties with other topographical features, dense vegetation, erosion etc.The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

The Goldeneye project combines different sensing technologies with proximal sensing to produce reference calibrated mineralogical maps through data fusion. In order to develop mineral detection applications, rock specimens, taken from an outcrop in Bockau (Erzgebirge, Germany), are analyzed with active hyperspectral scanning (AHS) as well as portable X-ray fluorescence (pXRF) devices. The received data is analyzed by means of artificial intelligence in order to develop an approach to automatically map the minerals with the samples. The analysis is carried out in advangeo® 2D Prediction, developed by Beak Consultants GmbH. Tin concentrations derived from pXRF measurements and AHS data from 2/3 of the specimen surface serve as training and validation data of the artificial intelligence algorithm (artificial neural networks). As a result, we developed a prediction model for the distribution of tin and its associated mineral cassiterite throughout the rock specimen, which allows to detect the mineral potential of hand specimens and larger outcrops in a fast and reliable manner.The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

The sulfidic sulfur contained in the host rocks and mining waste leads to strong acid mine drainage processes in the mining landscapes of Trepca, Kosovo and Pyhasalmi, Finnland. In the present, the water quality is usually monitored by discrete sampling and analysis of dissolved metal particles and other chemical parameters. Not only is this a cost- and time-consuming process, but the assessment takes place only on discrete locations.The main aim of this application is to elaborate the suitability of multispectral remote sensing (R/S) data from different sensors for area-wide identification and quantitative mapping of Acid Mine Drainage (AMD) constituents such as dissolved iron concentration (Fe3+), pH value etc. in water bodies. The potential for mining waste to be subject to AMD processes is also being investigated through area-wide quantitative mapping of the sulfate content (SO₄2-) in solid ground.In this framework, water and solid ground samples were collected to calibrate and validate the supervised machine learning algorithm of Artificial Neural networks (ANN), used for the identification of dependencies between the multispectral R/S data and the ground measurements. The ANNs of multilayer perceptron type (MLP) is implemented in the advangeo® 2D Prediction software from Beak Consultants GmbH (www.advangeo.com). The modelling and prediction software analyses complex non-linear relationships between a wide variety of spatial controlling parameters and natural complex processes or occurrences, by using methods of artificial intelligence within a Geographic Information System (GIS) environment.In the mining landscapes of Artana 1 & 2 and Kelmend, AKG has allocated and analysed about 20 water samples and 15 soil samples between May – August 2022 in two field campaigns, whereas low pH values (3 – 4), dissolved iron concentrations up to 25 mg/L and sulfate contents up to 28474 mg/kg have been recorded. Because of the small-scale features in the mining landscapes, high-resolution multispectral images from Worldview-3 and time-series of drone-based acquisitions are used as controlling parameters for the modelling process.In the tailing pond of Pyhasalmi and the surrounding water environment, the Oulu University has allocated and analysed about 60 water samples between June – October 2022 in two field campaigns. Low pH values (3 – 4), dissolved iron concentrations up to 1800 mg/L and sulfate contents up to 2200 mg/l have been recorded. In this case, medium-resolution multispectral images from Sentinel-2 (Level-1C TOA and Level-2A BOA products) and high-resolution images from Worldview-3 are used as controlling parameters for the modelling process.In all scenarios, the imagery was acquired during similar time frames as the sampling, to ensure that the measured water / soil grounds parameters correspond to the surface reflectance information.In the study, advantages and limitations of different multispectral imaging sensors are elaborated. The newly established dependencies from the ANN models can be used to perform area-wide monitoring of AMD processes in time-series, drastically reducing the need for terrestrial measurements in the future.The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

To ensure Europe increases its domestic production of high quality and responsibly produced raw materials, the development of innovative technologies for 3D geological modeling in mineral exploration is paramount. The Erzgebirge in Germany provides an excellent framework to showcase the application of artificial intelligence and in particular Artificial Neural Networks (ANN) for 3D mineral prospectivity mapping. The Erzgebirge belongs to the Variscan Belt, withholding 800 years of mining history and it is also famous for Ag, Sn, W, Fe, Cu, Li mineralizations among others. The Bockau deposit is located at the western section of the Erzgebirge. The target area is a Paleozoic metasediment body that was formed during the Variscan orogeny. The metasediment body consists primarily of alternating micaschist, phyllite and quartzite and dips mostly 25° to 240° SW. The metasediment is surrounded by Late Variscan plutons which partly led to contact metamorphic zones. In addition there is a large Quartzite body which was mined near to the surface in the 17th century for Sn, following a stratiform tin anomaly which can reach up to 4000 ppm Sn. Thanks to the long mining history, the Bockau deposit condenses a large amount of geological, geochemical, geophysical and mineral data. To increase mineralogical knowledge of the deposit and to help identify drilling targets, a hybrid approach for 3D mineral predictivity mapping is implemented. Potentially mineralisation-controlling factors are identified in knowledge-driven genetic exploration models, taking into account the borehole data, major faults, electromagnetic data, intrusive bodies, contact metamorphic zones and lithological borders, followed by data-driven weighted ANN predictive modelling implemented in the in-house developed advangeo® 3D Prediction Software. The predictive model is guided by structural variables such as the euclidean distance to fault planes, lithological surfaces and to metamorphic contact zones. The model is also constrained by geophysical data by a magnetic susceptibility model obtained from an airborne magnetic data inversion. Finally, Sn anomaly data from boreholes is implemented as training data for the prediction. The results show the probability distribution of Sn mineralisation occurrence in 3D over a voxel model formed by blocks of approximately 684 m3 13(x), 13.5(y) and 4(z), increasing the mineralogical knowledge of the deposit and guiding exploration efforts complementing the decision making process for drilling new targets. The results are validated by iteratively implementing the jackknife method, splitting the training data into validation and training subsets. The first prediction iteration is performed with a subset containing 77 % of the Sn content data from boreholes as training data, followed by 50 and 30 % subsets. Thus, allowing at each iteration to perform a quantitative evaluation of the prediction by comparing the validation subset with the Sn content of the borehole that was not used for the prediction. The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

Lithium (Li) was recently added to the list of critical raw materials by the European Union due to its significance for the green energy transition. Thus, the development of new toolchains to make Li exploration more economic and more effective is needed. Stream sediment analysis can play an important part in these new tool chains. In this work, two historical stream sediment datasets covering parts of the Fregeneda-Almendra pegmatite field in the Douro region (Portugal) were reprocessed considering two distinct approaches: spatial interpolation through inverse distance weighting (IDW) and the catchment basin approach using the concentration area (C-A) fractal analysis. The following objectives were delineated: (i) determine pathfinder elements for Li, considering relevant associations in the mineralization sources; (ii) compare the performance of both approaches; (iii) identify new target areas for Li. In the case of spatial interpolation, the highest Li values were associated to granites although the use of key elements allowed lithological discrimination and the delineation of target areas. In the catchment basin approach, fractal analysis proved to be effective in decreasing the number of areas of interest with high accuracy (>75%) when considering the previously mapped Li-pegmatites. One of the limitations identified was the number of anomalous basins related to the granites, despite the use of pathfinder elements allowing discriminating granite- from pegmatite-related Li anomalies. Comparing the two approaches, the spatial interpolation method is more adequate for the early stages of exploration (reconnaissance), while the catchment basin approach is more suited for prospect-scale exploration. Field validation of the results identified one pegmatite containing Li mineralization and three others with favorable signs for Li mineralization in the Douro region.

TRIM4Post-Mining is a H2020/RFCS-funded project that brings together a consortium of European experts from industry and academia to develop an integrated information modelling system. This is designed to support decision making and planning during the transition from coal exploitation to a revitalized post-mining landscape, enabling infrastructure development for agricultural and industrial utilization, and contributing to the recovery of energy and materials from coal mining dumps. The smart system will be founded upon a high-resolution spatiotemporal database, utilizing state-of-the-art multi-scale and multi-sensor monitoring technologies that characterize dynamic processes in coal waste dumps related to timely, dependent deformation and geochemical processes. It will integrate efficient methods for operational and post-mining monitoring, comprehensive spatiotemporal data analytics, feature extraction, and predictive modelling; this will allow for the identification of potential contamination areas and the forecasting of geotechnical risks and ground conditions. For the interactive exploration of alternative land-use planning scenarios—in terms of residual risks, technical feasibility, environmental and social impact, and affordability—up-to-date data and models will be embedded in an interactive planning system based on Virtual Reality and Augmented Reality technology, forming a TRIM—a Transition Information Modelling System. This contribution presents the conceptual approach and main constituents, and describes the state-of-the-art and detailed anticipated methodological approach for each of the constituents. This is supported by the presentation of the first results and a discussion of future work. An anticipated second contribution will focus on the main findings, technology readiness and a discussion of future work.

The growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and exploration are necessary. Artificial neural networks (ANNs) provide powerful tools for exploration target identification. They can be cost-effectively applied in various geological settings. This article presents an integrated approach of Li exploration targeting using ANNs for data interpretation. Based on medium resolution geological maps (1:50,000) and stream sediment geochemical data (1 sample per 0.25 km2), the Li potential was calculated for an area of approximately 1200 km2 in the surroundings of Bajoca Mine (Northeast Portugal). Extensive knowledge about geological processes leading to Li mineralisation (such as weathering conditions and diverse Li minerals) proved to be a determining factor in the exploration model. Furthermore, Sentinel-2 satellite imagery was used in a separate ANN model to identify potential Li mine sites exposed on the ground surface by analysing the spectral signature of surface reflectance in well-known Li locations. Finally, the results were combined to design a final map of predicted Li mineralisation occurrences in the study area. The proposed approach reveals how remote sensing data in combination with geological and geochemical data can be used for delineating and ranking exploration targets of almost any deposit type.

Self-organizing maps (SOM) are a useful tool to analyze and interpret gridded datasets like potential field or stream sediment geochemistry data. The data are transformed from geographic space to SOM space where they can be clustered according to overall similarity. By transforming the clusters back to geographic space, geological interpretation of the clusters is facilitated. We present the application of a multilayer perceptron (MLP) in SOM space to produce mineral predictive maps. The reduced number of grid cells in SOM space greatly enhances the performance of the MLP and the tolerance to noise in the input data, compared to an application of the MLP to the original data. The method is applied to tin skarn deposits in the German part of the Erzgebirge. The training and validation data required for the MLP are compiled from mining and exploration records. The input data for the SOM are reprocessed gravimetric, magnetic, stream sediment geochemistry, geologic and tectonic data sets. Potentially ore-controlling spatial relationships, such as the distance to different types of partly covered granite intrusions, are derived from a regional scale 3D geological model. The resulting mineral prediction map allows the definition of exploration zones for detailed studies. The paper has been compiled in the frame of "NEXT - New EXploration Technologies" project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776804.

Improved use of Earth observation, sensor and positioning data can offer additional exploitation and environmental control and increase the productivity of mines. The EU-funded Goldeneye project develops a Golden AI platform to allow satellites, drones and in situ sensors to collect high-resolution data from an entire mine. This data will be processed and converted into actionable intelligence in new tools offering improved safety, environmental observation, exploitation and increased extraction in pilot mines. The project will combine remote sensing and positioning technologies to take advantage of Earth observation and Earth GNSS data together with data fusion and processing powered by data analytics and machine learning algorithms. The benefits of Golden AI platform will be demonstrated in mining pilots in Bulgaria, Finland, Germany, Kosovo and Romania.

The licence areas for the exploration of manganese nodule fields in the equatorial Pacific Ocean cover 75,000 km(2) each. The purpose of this study was to predict the nodule abundance of nodule fields over an entire licence area using artificial neural network statistics. Bathymetry and backscatter information of the seafloor and different derived datasets, as well as sampling point data (box core stations), were used as model input data. Based on the prediction results, mineral resources of manganese nodules at different cut-off grades were calculated and the estimated resources were classified according to international codes. In principle, the estimation of tonnages of metals such as nickel, copper, cobalt, manganese, molybdenum, etc. is also possible based on the prediction results and the average metal contents of the nodules.