RESenv: A Realistic Earthquake Simulation Environment based on Unreal Engine

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dc.contributor.authorSun, Yitongen_US
dc.contributor.authorWang, Hanchunen_US
dc.contributor.authorZhang, Zhejunen_US
dc.contributor.authorDiels, Cyrielen_US
dc.contributor.authorAsadipour, Alien_US
dc.contributor.editorPelechano, Nuriaen_US
dc.contributor.editorLiarokapis, Fotisen_US
dc.contributor.editorRohmer, Damienen_US
dc.contributor.editorAsadipour, Alien_US
dc.date.accessioned2023-10-02T08:17:26Z
dc.date.available2023-10-02T08:17:26Z
dc.date.issued2023
dc.description.abstractEarthquakes 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.en_US
dc.description.sectionheadersNovel Technologies for Digital Avatars and Animation
dc.description.seriesinformationInternational Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET)
dc.identifier.doi10.2312/imet.20231259
dc.identifier.isbn978-3-03868-233-2
dc.identifier.pages65-74
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/imet.20231259
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/imet20231259
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Simulation environments; Computer vision; Human-centered computing -> Visualization toolkits
dc.subjectComputing methodologies
dc.subjectSimulation environments
dc.subjectComputer vision
dc.subjectHuman centered computing
dc.subjectVisualization toolkits
dc.titleRESenv: A Realistic Earthquake Simulation Environment based on Unreal Engineen_US
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IMET 2023: International Conference on Interactive Media, Smart Systems and Emerging Technologies