Integrating Geoscientific Concepts in Prognostic Modeling for Immersive Situation Representation: Enhancements from the oKat-SIM Project

Effective crisis management requires timely and accurate decision support, leveraging advanced computational methods and geoscientific insights. This study focuses on enhancing decision support for flood and landslide scenarios by integrating geoscientific concepts into prognosis modeling within immersive situation representation frameworks. Building upon the experiences and outcomes of the oKat-SIM project (optimized disaster response through simulation), we demonstrate how coupling high-performance computing with Geographic Information Systems (GIS) can improve real-time response capabilities in civil protection. The project aligns with the foundational goal stated in the Leopoldina report, emphasizing the significance of geoscientific process understanding in decision-making to prepare for, mitigate, and manage natural disasters effectively.

Our approach transcends traditional mapping by utilizing immersive and dynamic 3D representations through synchronized augmented reality (AR) glasses, allowing crisis management teams to maintain interpersonal communication while interacting with floating 3D scenario displays. This integration augments situational awareness and facilitates decision-making in high-pressure environments, such as crisis management centers. The involvement of end-users - both, operational and administrative personnel from municipalities and regional authorities - is crucial throughout the process of application development, allowing iterative improvements driven by real-world feedback.

Technical building aspects include: real-time landslide susceptibility and run-out modelling tightly coupled with GIS-based preprocessing and executed inside a Unity-based immersive runtime, enabling near-real-time scenario updates driven by HPC- and AI-assisted workflows. Advanced rendering techniques such as Gaussian Splatting, multi-resolution terrain streaming, and federated data fusion are leveraged to efficiently integrate remote sensing data, simulation outputs, and uncertainty layers into synchronized AR/3D views, providing scalable, low-latency situational awareness and decision support for time-critical crisis management.

Our case studies demonstrate the effective visualization of historical and potential disaster scenarios, fostering deeper understanding of complex interdependencies and enabling faster, informed decision-making. This interdisciplinary effort bridges geoscience and computational technologies, advancing operational platforms for flood and landslide preparedness and response, and fostering collaborative advancements for modern crisis management.