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.

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.

This article reflects discussions German and South African Earth scientists, statisticians and risk analysts had on occasion of two bilateral workshops on Data Integration Technologies for Earth System Modelling and Resource Management. The workshops were held in October 2012 at Leipzig, Germany, and April 2013 at Pretoria, South Africa, and were attended by about 70 researchers, practitioners and data managers of both countries. Both events were arranged as part of the South African-German Year of Science 2012/2013. The South African National Research Foundation (NRF, UID 81579) has supported the two workshops as part of the South African–German Year of Science activities 2012/2013 established by the German Federal Ministry of Education and Research and the South African Department of Science and Technology.

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.

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.

Fuzzy logic-based mineral potential mapping was applied to the Tisová–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 Tisová 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.