You probably don't give much thought to the device that you're reading this article on, as long as it looks good and keeps working. But the elements that power and run it are the subject of an escalating struggle between the world's two biggest economies - the US and China - with African countries in the eye of the storm. The African continent is rich in critical minerals and metals - like lithium, rare earths, cobalt and tungsten - which are vital to making and running our personal tech. Such materials are also essential for everything from electric vehicles, to AI data centres, and weapon systems. China has long been the biggest player in the global market for critical minerals and metals.

--The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UA V-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accurate information for open-pit mining environments. The proposed pipeline includes geo-referencing, 3D reconstruction, and object localization, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real-world industrial contexts. While the current implementation operates in post-flight batch mode, it lays the foundation for real-time extensions. The system contributes to the development of AI-enhanced remote sensing in mining by demonstrating a scalable and field-validated geospatial data workflow that supports situational awareness and infrastructure safety. HE mining industry is significantly transforming by integrating emerging digital technologies. One of the primary challenges facing this sector is the lack of high-precision real-time geospatial data to support decision-making in exploration, extraction, and safety monitoring [1], [2]. Traditional data collection methods often involve high costs, time-consuming processes, and potential safety risks. The proposed approach enables the detection of key objects using onboard cameras and deep learning techniques, followed by their projection onto the 3D map for enhanced situational awareness. Additionally, the system leverages geo-referenced images to support visual navigation, improving UA V positioning within the mining environment. Balaska(*corresponding author), I.T Papapetros, K.M Oikonomou and A. Gasteratos are with the Department of Production and Management Engineering, Democritus University of Thrace, Xanthi, Greece. L. Bampis is with the Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece.

Accurate short-term forecasting of hauling-fleet capacity is crucial in open-pit mining, where weather fluctuations, mechanical breakdowns, and variable crew availability introduce significant operational uncertainties. We propose a deep-learning framework that blends real-world operational records (high-resolution rainfall measurements, fleet performance telemetry) with synthetically generated mechanical-breakdown scenarios to enable the model to capture fluctuating high-impact failure events. We evaluate two architectures: an XGBoost regressor achieving a median absolute error (MedAE) of 14.3 per cent and a Long Short-Term Memory network with a MedAE of 15.1 per cent. Shapley Additive exPlanations (SHAP) value analyses identify cumulative rainfall, historical payload trends, and simulated breakdown frequencies as dominant predictors. Integration of simulated breakdown data and shift-planning features notably reduces prediction volatility. Future work will further integrate maintenance-scheduling indicators (Mean Time Between Failures, Mean Time to Repair), detailed human resource data (operator absenteeism, crew efficiency metrics), blast event scheduling, and other operational constraints to enhance forecast robustness and adaptability. This hybrid modelling approach offers a comprehensive decision-support tool for proactive, data-driven fleet management under dynamically uncertain conditions.

--Mining process optimization, particularly truck dispatch scheduling, is a critical factor in enhancing the efficiency of open-pit mining operations. However, the dynamic and stochastic nature of mining environments--characterized by uncertainties such as equipment failures, truck maintenance, and variable haul cycle times--poses significant challenges for traditional optimization methods. While Reinforcement Learning (RL) has demonstrated promise in adaptive decision-making for mining logistics, its practical deployment requires rigorous evaluation in realistic and customizable simulation environments. T o address this challenge, we introduce Mining-Gym, a configurable, open-source benchmarking environment designed for training, testing, and comparing RL algorithms in mining process optimization. Built on Discrete Event Simulation (DES) and seamlessly integrated with the OpenAI Gym interface, Mining-Gym offers a structured testbed that enables the direct application of advanced RL algorithms from Stable Baselines. The framework models key mining-specific uncertainties, such as equipment failures, queue congestion, and stochasticity of mining processes, ensuring a realistic and adaptive learning environment. Additionally, a graphic user interface (GUI) for easy parameter selection for mine-site configuration, comprehensive data logging system, a built-in KPI dashboard and real-time representative visualization of mine-site enables in-depth performance analysis, facilitating standardized, reproducible evaluation across multiple RL strategies and baseline heuristics. INING process optimization aims to enhance efficiency and productivity by improving resource allocation, equipment scheduling, and material handling. However, these operations are highly complex, influenced by dynamic factors such as equipment failures, fluctuating ore quality, and unpredictable environmental conditions. Traditional optimization methods, such as linear programming and heuristics, struggle to adapt in real time, leading to inefficiencies and increased costs.

The world is entering an unprecedented period of critical mineral demand, driven by the global transition to renewable energy technologies and electric vehicles. This transition presents unique challenges in mineral resource development, particularly due to geological uncertainty-a key characteristic that traditional supply chain optimization approaches do not adequately address. To tackle this challenge, we propose a novel application of Partially Observable Markov Decision Processes (POMDPs) that optimizes critical mineral sourcing decisions while explicitly accounting for the dynamic nature of geological uncertainty. Through a case study of the U.S. lithium supply chain, we demonstrate that POMDP-based policies achieve superior outcomes compared to traditional approaches, especially when initial reserve estimates are imperfect. Our framework provides quantitative insights for balancing domestic resource development with international supply diversification, offering policymakers a systematic approach to strategic decision-making in critical mineral supply chains.

TL;DR: Landmines pose a persistent threat and hinder development in over 70 war-affected countries. Humanitarian demining aims to clear contaminated areas, but progress is slow: at the current pace, it will take 1,100 years to fully demine the planet. In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. The system is being tested in Afghanistan and Colombia, where it has already led to the discovery of new landmines. Anti-personnel landmines are explosive devices hidden in the ground designed to explode by proximity or contact and with the capacity to kill, disable or cause harm to humans (Figure 1). The mere threat of landmine contamination in a territory not only endangers the physical well-being of affected populations but also results in a loss of forest areas, reduction of productive land, exacerbation of social vulnerability, delay of infrastructure development, and damage of natural, physical, and social capital.

This study aims to establish the causal relationship network between various factors leading to workday loss in underground coal mines using a novel causal artificial intelligence (AI) method. The analysis utilizes data obtained from the National Institute for Occupational Safety and Health (NIOSH). A total of 101,010 injury records from 3,982 unique underground coal mines spanning the years from 1990 to 2020 were extracted from the NIOSH database. Causal relationships were analyzed and visualized using a novel causal AI method called Grouped Greedy Equivalence Search (GGES). The impact of each variable on workday loss was assessed through intervention do-calculus adjustment (IDA) scores. Model training and validation were performed using the 10-fold cross-validation technique. Performance metrics, including adjacency precision (AP), adjacency recall (AR), arrowhead precision (AHP), and arrowhead recall (AHR), were utilized to evaluate the models. Findings revealed that after 2006, key direct causes of workday loss among mining employees included total mining experience, mean office employees, mean underground employees, county, and total mining experience (years). Total mining experience emerged as the most influential factor, whereas mean employees per mine exhibited the least influence. The analyses emphasized the significant role of total mining experience in determining workday loss. The models achieved optimal performance, with AP, AR, AHP, and AHR values measuring 0.694, 0.653, 0.386, and 0.345, respectively. This study demonstrates the feasibility of utilizing the new GGES method to clarify the causal factors behind the workday loss by analyzing employment demographics and injury records and establish their causal relationship network.

Geomechanical monitoring of a rock massif is an actively developing branch of geomechanics. It is almost impossible to single out a methodology and approaches for data collection and analysis in developing seismic monitoring systems. In the process of mining in rock massif, changes in the state of structural inhomogeneities are most clearly manifested. Existing natural structural inhomogeneities are revealed, there are movements in discontinuous disturbances, and new technogenic disturbances are formed, which are accompanied by a change in the natural stress state of various blocks of the massif. An important task is to develop a mining forecasting model that can take into account the structural heterogeneity of the rock massif and select the necessary forecast horizon depending on monitoring data The developed method of evaluating the results of monitoring geomechanical processes in the rock massif allowed us to forecast of zones of possible rock bursts.

Domestic giant Tata Steel on Wednesday said it has inked a pact with a Bengaluru-based startup for drone-based mining solutions for effective mine management. The primary goal of this collaboration is to jointly develop and offer sustainable and end-to-end integrated solutions that will focus on efficiency, safety, and productivity of open cast mining operations. "Tata Steel has signed a Memorandum of Understanding with Aarav Unmanned Systems, a Bangaluru-based startup, providing end-to-end drone solutions... for effective mine management," the company said in a statement. Tata Steel will also work jointly with AUS to provide exclusive drone-based solutions, including mine analytics and geo-technical mapping, to Tata Steel group companies across mining locations in India, it said. On the partnership, D B Sundara Ramam, Vice President, Raw Materials, Tata Steel, said: "Drone survey enabled digitalisation and other technology will assist in gathering impactful and actionable insights. We see enormous potential in redefining core mining processes such as exploration and mine planning using drone data and adequate analytics."

Slope stability is essential in mining operations since slope failure endangers safety and productivity. The complexity of conventional geotechnical methods makes slope failure prediction challenging. Artificial intelligence (AI) has helped mining companies forecast slope failures quickly and efficiently through detailed analysis. Due to the development of more advanced mining techniques and the growing demand for mineral resources, most mines are constructed to extract more minerals from steeper or deeper areas. The steeper slope angle makes these mines more vulnerable to slope failure. It can cause injury to workers, damage to mine equipment, and halt production, negatively influencing mining productivity.