Project

A Generative and Agentic AI-Powered Voxel Simulation Framework for Rapid 3D Wildfire Spread Prediction


Host university

INDUSTRY PARTNERS

THEME ALIGNMENT

Ubiquitous Sensing, Intelligent and Adaptive Systems

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INDUSTRY PROBLEM

The rapid expansion of LiDAR, BIM, photogrammetry, and UAV mapping has made high-resolution 3D environmental data widely available for urban planning and hazard modelling. However, current operational wildfire models remain 2D surface-based such as SPARK), unable to ingest 3D grids or capture vertical structure, accurate estimates of fuel distribution, or mixed urban–bush interactions. Our early physics-based voxel fire prototype quickly hit memory limits beyond ~300 × 300 × 150 voxels due to the storage demands of multiple physical fields and solver buffers. It also struggles to incorporate dynamic drivers, such as wind, fuel, moisture, and atmospheric conditions, because the process of simulation cannot be interrupted. As a result, there is no efficient 3D framework for modelling wildfire dynamics in large, mixed voxel environments.


CONTACT CONTRIBUTORS

Sisi Zlatanova

RIIS Hub Lead Chief Investigator
University of New South Wales
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Ismet Canbulat

Chief Investigator
UNSW Sydney
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Haowen Xu

Research Fellow
UNSW Sydney
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Yasuhito Niina

Director of the Advanced Technology Research Laboratory
Asia Air Survey Co., Ltd.
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