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N.C.A. Driessen
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Wildfires are increasing in both frequency and severity due to climate change, which increases the need for accurate and scalable fire modelling techniques. While existing simulation methods range from statistical models to physics-based approaches, most high-fidelity models rely on idealized, synthetic environments with complete scene knowledge. This limits their applicability to real-world forests, as well as evaluation opportunities.
This thesis addresses this gap by investigating wildfire simulation directly on real-world scene reconstructions using 3D Gaussian Splatting (3DGS). On the Feature Splatting framework, a complete pipeline is developed that reconstructs forest scenes from aerial imagery and augments them with semantic and material information.
A preprocessing framework is introduced to refine these reconstructions. It includes noise filtering, semantic classification, procedural stem insertion, and material column completion. On this representation, a physics-based, particle-driven fire simulation model is implemented, which models heat transfer, ignition, combustion, and fire spread directly on Gaussian primitives. The system is integrated into the Nerfstudio framework.
The evaluation demonstrates that the proposed simulation reproduces several characteristic wildfire behaviours reported in the literature. Fire spread increases with vegetation density and is strongly influenced by wind direction and speed. Slope experiments confirm that fire propagates faster uphill than downhill, with an accelerating increase in spread rate for steeper upward slopes. Wind-driven behaviour is qualitatively realistic, including faster burnout under stronger wind conditions. The simulation is stable across repeated runs and performs consistently on synthetic scenes.
In addition to standard evaluations, this thesis also introduces novel testing scenarios, such as forest obstruction experiments and burnt mass estimation. Results indicate that wind influence is slightly overestimated, particularly in firebreak scenarios where residual flammable material enables fire to cross barriers at lower wind speeds than expected. Burnt mass estimation reveals that the current model underestimates total biomass loss. Further calibration of material properties and combustion parameters is required.
Finally, the simulation is applied to real-world drone data. The system is capable of running wildfire simulations on reconstructed forest scenes, but challenges remain, such as noise in the data, limitations in semantic classification, and scalability issues, as these scenes are significantly larger
Overall, this work presents a novel and robust pipeline for wildfire simulation on Gaussian Splatting-based scene representations. ...
This thesis addresses this gap by investigating wildfire simulation directly on real-world scene reconstructions using 3D Gaussian Splatting (3DGS). On the Feature Splatting framework, a complete pipeline is developed that reconstructs forest scenes from aerial imagery and augments them with semantic and material information.
A preprocessing framework is introduced to refine these reconstructions. It includes noise filtering, semantic classification, procedural stem insertion, and material column completion. On this representation, a physics-based, particle-driven fire simulation model is implemented, which models heat transfer, ignition, combustion, and fire spread directly on Gaussian primitives. The system is integrated into the Nerfstudio framework.
The evaluation demonstrates that the proposed simulation reproduces several characteristic wildfire behaviours reported in the literature. Fire spread increases with vegetation density and is strongly influenced by wind direction and speed. Slope experiments confirm that fire propagates faster uphill than downhill, with an accelerating increase in spread rate for steeper upward slopes. Wind-driven behaviour is qualitatively realistic, including faster burnout under stronger wind conditions. The simulation is stable across repeated runs and performs consistently on synthetic scenes.
In addition to standard evaluations, this thesis also introduces novel testing scenarios, such as forest obstruction experiments and burnt mass estimation. Results indicate that wind influence is slightly overestimated, particularly in firebreak scenarios where residual flammable material enables fire to cross barriers at lower wind speeds than expected. Burnt mass estimation reveals that the current model underestimates total biomass loss. Further calibration of material properties and combustion parameters is required.
Finally, the simulation is applied to real-world drone data. The system is capable of running wildfire simulations on reconstructed forest scenes, but challenges remain, such as noise in the data, limitations in semantic classification, and scalability issues, as these scenes are significantly larger
Overall, this work presents a novel and robust pipeline for wildfire simulation on Gaussian Splatting-based scene representations. ...
Wildfires are increasing in both frequency and severity due to climate change, which increases the need for accurate and scalable fire modelling techniques. While existing simulation methods range from statistical models to physics-based approaches, most high-fidelity models rely on idealized, synthetic environments with complete scene knowledge. This limits their applicability to real-world forests, as well as evaluation opportunities.
This thesis addresses this gap by investigating wildfire simulation directly on real-world scene reconstructions using 3D Gaussian Splatting (3DGS). On the Feature Splatting framework, a complete pipeline is developed that reconstructs forest scenes from aerial imagery and augments them with semantic and material information.
A preprocessing framework is introduced to refine these reconstructions. It includes noise filtering, semantic classification, procedural stem insertion, and material column completion. On this representation, a physics-based, particle-driven fire simulation model is implemented, which models heat transfer, ignition, combustion, and fire spread directly on Gaussian primitives. The system is integrated into the Nerfstudio framework.
The evaluation demonstrates that the proposed simulation reproduces several characteristic wildfire behaviours reported in the literature. Fire spread increases with vegetation density and is strongly influenced by wind direction and speed. Slope experiments confirm that fire propagates faster uphill than downhill, with an accelerating increase in spread rate for steeper upward slopes. Wind-driven behaviour is qualitatively realistic, including faster burnout under stronger wind conditions. The simulation is stable across repeated runs and performs consistently on synthetic scenes.
In addition to standard evaluations, this thesis also introduces novel testing scenarios, such as forest obstruction experiments and burnt mass estimation. Results indicate that wind influence is slightly overestimated, particularly in firebreak scenarios where residual flammable material enables fire to cross barriers at lower wind speeds than expected. Burnt mass estimation reveals that the current model underestimates total biomass loss. Further calibration of material properties and combustion parameters is required.
Finally, the simulation is applied to real-world drone data. The system is capable of running wildfire simulations on reconstructed forest scenes, but challenges remain, such as noise in the data, limitations in semantic classification, and scalability issues, as these scenes are significantly larger
Overall, this work presents a novel and robust pipeline for wildfire simulation on Gaussian Splatting-based scene representations.
This thesis addresses this gap by investigating wildfire simulation directly on real-world scene reconstructions using 3D Gaussian Splatting (3DGS). On the Feature Splatting framework, a complete pipeline is developed that reconstructs forest scenes from aerial imagery and augments them with semantic and material information.
A preprocessing framework is introduced to refine these reconstructions. It includes noise filtering, semantic classification, procedural stem insertion, and material column completion. On this representation, a physics-based, particle-driven fire simulation model is implemented, which models heat transfer, ignition, combustion, and fire spread directly on Gaussian primitives. The system is integrated into the Nerfstudio framework.
The evaluation demonstrates that the proposed simulation reproduces several characteristic wildfire behaviours reported in the literature. Fire spread increases with vegetation density and is strongly influenced by wind direction and speed. Slope experiments confirm that fire propagates faster uphill than downhill, with an accelerating increase in spread rate for steeper upward slopes. Wind-driven behaviour is qualitatively realistic, including faster burnout under stronger wind conditions. The simulation is stable across repeated runs and performs consistently on synthetic scenes.
In addition to standard evaluations, this thesis also introduces novel testing scenarios, such as forest obstruction experiments and burnt mass estimation. Results indicate that wind influence is slightly overestimated, particularly in firebreak scenarios where residual flammable material enables fire to cross barriers at lower wind speeds than expected. Burnt mass estimation reveals that the current model underestimates total biomass loss. Further calibration of material properties and combustion parameters is required.
Finally, the simulation is applied to real-world drone data. The system is capable of running wildfire simulations on reconstructed forest scenes, but challenges remain, such as noise in the data, limitations in semantic classification, and scalability issues, as these scenes are significantly larger
Overall, this work presents a novel and robust pipeline for wildfire simulation on Gaussian Splatting-based scene representations.
Creating a local model of a remote environment is a way to reduce latency in tactile internet. This local model contains properties that are estimated by a nonintrusive estimation method. To prevent the model from deviating increasingly from reality, the estimates should be updated once an interaction begins. This research paper investigates how the physical properties of a remote object can be estimated. This is done by exerting forces and observing the resulting motion. The physical properties that are updated include the mass and center of mass. These values are calculated to converge toward the actual value. The mass can be estimated by observing the linear motion, for which the game engine Unity is used. For the center of mass estimation, it is advised to take a different approach, since the values received by Unity were not enough to base an estimate on. This paper also investigates the influence of update frequency on estimation accuracy and the inaccuracies that the Unity physics engine presents.
...
Creating a local model of a remote environment is a way to reduce latency in tactile internet. This local model contains properties that are estimated by a nonintrusive estimation method. To prevent the model from deviating increasingly from reality, the estimates should be updated once an interaction begins. This research paper investigates how the physical properties of a remote object can be estimated. This is done by exerting forces and observing the resulting motion. The physical properties that are updated include the mass and center of mass. These values are calculated to converge toward the actual value. The mass can be estimated by observing the linear motion, for which the game engine Unity is used. For the center of mass estimation, it is advised to take a different approach, since the values received by Unity were not enough to base an estimate on. This paper also investigates the influence of update frequency on estimation accuracy and the inaccuracies that the Unity physics engine presents.