Environmental and lithospheric processes are driven by complex interactions among geological, geophysical, hydrological, climatic, and anthropogenic factors acting across multiple spatial and temporal scales. Recent advances in Earth Observation technologies, sensor development, autonomous platforms, and computational methods are enabling unprecedented capabilities for monitoring, modeling, and forecasting natural processes and hazards.
This session welcomes contributions presenting innovative approaches for the observation, characterization, and modeling of environmental and lithospheric systems through the integration of satellite, airborne, UAV-borne, ground-based, and proximal sensing technologies. Particular emphasis is placed on multi-platform and multi-sensor strategies that combine Earth Observation data with geophysical, geodetic, environmental, and in situ measurements to improve process understanding and hazard assessment.
The session aims to highlight recent developments in advanced instrumentation, monitoring networks, real-time and near-real-time acquisition systems, and intelligent data processing workflows. Contributions addressing machine learning, deep learning, data fusion, data assimilation, anomaly detection, and automated interpretation of large and continuous datasets are especially encouraged. Special attention will be given to approaches supporting rapid decision-making, forecasting, and early-warning applications.
We particularly welcome studies focused on monitoring and modeling volcanic activity, active tectonics, seismic and crustal deformation, land subsidence, slope instability, floods, coastal dynamics, climate-related processes, energy exploitation, and other natural or anthropogenic hazards. Contributions employing advanced analytical and numerical approaches, including inverse modeling, multiphysics simulations, and finite element methods, are also encouraged.
The session brings together researchers from geophysics, geology, seismology, geodesy, remote sensing, environmental sciences, geomatics, engineering, artificial intelligence, and data science to foster interdisciplinary collaboration. The objective is to advance the next generation of integrated Earth Observation systems, intelligent monitoring frameworks, and predictive modeling approaches for environmental and lithospheric processes.