The exposure of metal sulfides to air or water, either produced naturally or due to mining activities, can result in environmentally damaging acid mine drainage (AMD). This needs to be accurately monitored and remediated. In this study, we apply high-resolution unmanned aerial system (UAS)-based hyperspectral mapping tools to provide a useful, fast, and non-invasive method for the monitoring aspect. Specifically, we propose a machine learning framework to integrate visible to near-infrared (VNIR) hyperspectral data with physicochemical field data from water and sediments, together with laboratory analyses to precisely map the extent of acid mine drainage in the Tintillo River (Spain). This river collects the drainage from the western part of the Rio Tinto massive sulfide deposit and discharges large quantities of acidic water with significant amounts of dissolved metals (Fe, Al, Cu, Zn, amongst others) into the Odiel River. At the confluence of these rivers, different geochemical and mineralogical processes occur due to the interaction of very acidic water (pH 2.5–3.0) with neutral water (pH 7.0–8.0). This complexity makes the area an ideal test site for the application of hyperspectral mapping to characterize both rivers and better evaluate contaminated water bodies with remote sensing imagery. Our approach makes use of a supervised random forest (RF) regression for the extended mapping of water properties, using the samples collected in the field as ground-truth and training data. The resulting maps successfully estimate the concentration of dissolved metals and related physicochemical properties in water, and trace associated iron species (e.g., jarosite, goethite) within sediments. These results highlight the capabilities of UAS-based hyperspectral data to monitor water bodies in mining environments, by mapping their hydrogeochemical properties, using few field samples. Hence, we have demonstrated that our workflow allows the rapid discrimination and mapping of AMD contamination in water, providing an essential basis for monitoring and subsequent remediation.

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.

Heritage and culture tourism involve features that commemorate a valued past. Mining heritage tourism allows visitors to experience the past, guided by former mining landscapes and engaging interactively with material artifacts. This paper introduces the CoalHeritage European project, focusing on the promotion of coal mining heritage through the production and design of the European Visual Map Journal (EVMJ). The EVMJ is a user-friendly, web-based, interactive storytelling platform that supports the transfer of industrial and geoheritage from former coal mining areas. It aims to collect and disseminate heritage assets from post-mining coal areas, informing stakeholders and promoting these sites as tourist destinations. To further enhance public awareness, several ESRI StoryMaps web apps are being created to highlight specific features of each case study across Europe. The aim of this work is to introduce coal mining heritage as a new term, present the coal heritage platform and its importance for disseminating coal heritage aspects to the public, describe the methodology used for its design, and provide a brief overview of its evolving content.

Direct and indirect effects after mine operations cease operating must ideally be subjected to perpetual monitoring routines in order to detect possible risks or avoid adverse effects on the surrounding ecosystems at an early stage. In this contribution, mining subsidence lakes created inside the nature reserve Kirchheller Heide and Hilsfeld Forest are subjected to analysis for a long-term monitoring scheme. For this purpose, we employ high-resolution unmanned aerial system (UAS)-based multispectral and thermal mapping tools to provide a fast, non-invasive and multitemporal environmental monitoring method. Specifically, we propose to monitor vegetation evolution through multispectral analysis, biotypes identification using machine learning algorithms, and water surface extent detection, together with their thermal behavior. The aim of this contribution is to present the proposed workflow and first results to establish a baseline for future analyses and subsequent surveys for long-term multi-temporal monitoring.

The European Union’s climate-neutrality objective is reshaping coal-dependent regions, but transition pathways differ according to historical coal dependence, institutional capacity, national energy systems, and regional economic structures. Coal phase-outs represent not merely a sector-specific energy shift, but a systemic socio-spatial transformation that reconfigures regional planning and territorial dynamics. This study examines how governance coordination, phase-out clarity, labour-market instruments, innovation strategies, and post-mining land reuse operate across three major Central European coal regions: the Ruhr Area (Germany), Upper Silesia (Poland), and the Moravian–Silesian Region (Czechia). It applies a qualitative, structured comparative review based on academic literature, policy and strategic documents, official statistics, and three flagship redevelopment cases within the framework of the EU RFCS-funded RAISING project. The comparison shows that the Ruhr Area’s long-term, corporatist coordination has facilitated structural diversification and land reuse over several decades, though challenges regarding job quality and socio-spatial inequalities persist. In Upper Silesia, negotiated agreements foster regional consensus, while the multi-decade transition timeline reflects national energy-security priorities, supply-security constraints, and institutional complexity. In the Moravian–Silesian Region, early reactive restructuring in the 1990s contrasts with a more coordinated contemporary phase leveraging EU transition funding and strategic investment. Across all three cases, transition outcomes are mediated by place-specific conditions rather than formal closure speed alone. The study demonstrates that while core principles of early planning, multi-level coordination, and integrated asset reuse offer valuable insights, their application remains context-dependent, highlighting the necessity for place-sensitive European and national transition policies.

The use of hyperspectral imaging (HSI) at different scales has become a big ally for a large range of applications in remote sensing, especially within the mining life cycle. In this contribution, we explore the use of HSI sensing, particularly to monitor mine waste at different constellations that might face environmental damaging phenomena: Acid Mine Drainage (AMD). Failure to accurately monitor and remediate such a complex process, leads to long-term impacts on ecosystems and human health, in addition to significant financial consequences and reputational damage to operators. We propose a workflow to integrate hyperspectral visible to near-infrared (VNIR) data, together with mineralogical and geochemical data to precisely map the extent of acid mine drainage using machine learning algorithms. Collected data from the field and further laboratory analyses on a few specimens provide the ground-truth and training data to support the proposed mapping algorithms. Results consist of semi-quantified concentration of dissolved metals, physicochemical properties in water bodies, and associated AMD minerals sub-products (e.g., goethite, jarosite, schwertmannite) within mine waste materials, as well as mineralogical characterization for predictive modeling at laboratory scale. Regardless of the scale of acquisition, spectral imaging represents a cost-effective tool to enhance the quality of classical environmental analyses both in active and post-mining scenarios, which can increase the overall accuracy of the monitoring, allowing frequent and multi-temporal observations to reveal risk scenarios, take fast corrective actions and keep a continuous control.