  Earthquakes have a significant impact on societies and economies, driving the
need for effective search and rescue strategies. With the growing role of AI
and robotics in these operations, high-quality synthetic visual data becomes
crucial. Current simulation methods, mostly focusing on single building
damages, often fail to provide realistic visuals for complex urban settings. To
bridge this gap, we introduce an innovative earthquake simulation system using
the Chaos Physics System in Unreal Engine. Our approach aims to offer detailed
and realistic visual simulations essential for AI and robotic training in
rescue missions. By integrating real seismic waveform data, we enhance the
authenticity and relevance of our simulations, ensuring they closely mirror
real-world earthquake scenarios. Leveraging the advanced capabilities of Unreal
Engine, our system delivers not only high-quality visualisations but also
real-time dynamic interactions, making the simulated environments more
immersive and responsive. By providing advanced renderings, accurate physical
interactions, and comprehensive geological movements, our solution outperforms
traditional methods in efficiency and user experience. Our simulation
environment stands out in its detail and realism, making it a valuable tool for
AI tasks such as path planning and image recognition related to earthquake
responses. We validate our approach through three AI-based tasks: similarity
detection, path planning, and image segmentation.
