Our study addresses the need for universal monitoring solutions given the diverse environmental impacts of surface mining operations. We present a solution combining remote sensing and machine learning techniques, utilizing a dataset of over 2000 satellite images annotated with ten distinct labels indicating mining area components. We tested various approaches to develop comprehensive yet universal machine learning models for mining area segmentation. This involved considering different types of mines, raw materials, and geographical locations. We evaluated multiple satellite data set combinations to determine optimal outcomes. The results suggest that radar and multispectral data fusion did not significantly improve the models’ performance, and the addition of further channels led to the degradation of the metrics. Despite variations in mine type or extracted material, the models’ effectiveness remained within an Intersection over Union value range of 0.65–0.75. Further, in this research, we conducted a detailed visual analysis of the models’ outcomes to identify areas requiring additional attention, contributing to the discourse on effective mining area monitoring and management methodologies. The visual examination of models’ outputs provides insights for future model enhancement and highlights unique segmentation challenges within mining areas.

The article presents the impact of lignite mining on the environment associated with the introduction of hydro borehole mining (HBM) technology. The results are partially based on the HydroCoal Plus project results, where an environmental assessment of lignite HBM technology was performed. In order to reach the goals of the task, a life cycle assessment (LCA) study was used to assess selected environmental aspects of the HBM single-borehole lignite production process covering selected environmental impact categories such as energy consumption, fuel consumption, water consumption and carbon footprint. The LCA procedure was adapted in an innovative way, constituting another added value to the shown research. The second part of this paper describes opportunities identified by the authors to minimize the environmental impact of lignite production by implementing the HBM method compared to the conventional opencast method.

Former lignite mining regions across Europe present not only significant environmental challenges but also unique opportunities for climate-positive land use. These areas are often degraded due to soil compaction, contamination, altered hydrology, and landscape disruption. However, they also offer lands suitable for agricultural reclamation in terms of carbon farming and biomass production. The COFA project (From Coal to Farm) aims to promote evidence-based land management decisions, contributing to the just transition of former lignite-dependent regions. It promotes climate mitigation, renewable energy generation (biomass), and sustainable development.Our study develops a multi-criteria framework to assess the suitability of reclaimed lignite mining sites for energy crops and carbon farming, integrating multiple environmental factors. Key factors considered include terrain morphology, area, soil quality and degradation type, hydrological conditions, neighbouring land-uses, proximity to biomass power plants and the potential for alternative land uses such as food production, PV installations, or natural succession. The framework aims to identify sites where energy crops and carbon farming can be environmentally viable and socio-economically beneficial. Our research demonstrates how multi-criteria assessments can support the transformation of post mining regions into productive, climate-positive areas. It aligns with the objectives of the European Green Deal and broader sustainable redevelopment goals.