The transition towards sustainable and resilient raw materials supply requires improved monitoring and characterisation across the entire mining lifecycle, from exploration to closure and post-mining management. Earth Observation (EO), combined with in-situ measurements, geospatial technologies and advanced data analytics, is opening new possibilities for the continuous characterization and monitoring of areas of raw material potential, as well as active, inactive, abandoned and legacy mining sites, This includes the exploration of prospective areas, open pits, underground mining areas, waste rock dumps, tailings storage facilities and surrounding environments, supporting a comprehensive understanding of mining activities and their environmental impacts throughout the full mining cycle
This session brings together innovative EO products, sensor technologies, and modelling approaches for developing integrated monitoring frameworks that transform heterogeneous multi-source observations into actionable knowledge across the full raw materials and mining lifecycle. We particularly welcome interdisciplinary contributions addressing the characterization, monitoring, management, and risk assessment of prospective areas, mine waste sites, including tailings facilities and waste-rock deposits
The session welcomes contributions on
• Earth observation, geospatial approaches and sensor technologies across the full mining life cycle
• Ground deformation, dynamic hazard and geotechnical and risk monitoring and assessment of mining sites, tailings facilities, waste-rock deposits, and other mining-related infrastructure
• Environmental and sustainability monitoring, including land-use change, water, vegetation, contamination, and ecosystem impacts associated with mining and mine waste
• Multi-scale and multi-source data integration, combining satellite, airborne, UAV, and ground-based observations with geological, geochemical, geotechnical, and historical data for exploration, mine monitoring and post-mining assessment
• AI, ML, data fusion, and predictive modelling for automated, continuous, and near-real-time monitoring and assessment
• Data scalability, interoperability, and practical uptake of monitoring approaches, including their transferability across sites and regions and implementation by industry, authorities, and environmental managers