Fire in paradise

Hädrich, Torsten; Banuti, Daniel T.; Pałubicki, Wojciech; Pirk, Sören; Michels, Dominik L.

doi:10.1145/3476576.3476747

Cited by 4 publications

(10 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model is interactive and can simulate multiple trees at the same time. Hädrich et al [35] continued the previous research of Pirk et al as a wildfire simulation on the scale of entire forests by simulating the combustion process of each individual tree. This wildfire simulation focuses on the fire spread of the combustion of plants, considering heat transfer, char insulation, and mass loss.…”

Section: Three-dimensional Simulation Platformmentioning

confidence: 99%

SWIFT: Simulated Wildfire Images for Fast Training Dataset

Fernando,

Ghali,

Akhloufi

2024

Remote Sensing

View full text Add to dashboard Cite

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires.

show abstract

Section: Three-dimensional Simulation Platformmentioning

confidence: 99%

SWIFT: Simulated Wildfire Images for Fast Training Dataset

Fernando,

Ghali,

Akhloufi

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Another source of inspiration is the recent work in hybrid atmospheric simulation. For example, Fire in Paradise [HBP*21] couples a module‐based ecosystem with fluid solvers for the spread of fire, smoke, and clouds in the service of capturing wildfire dynamics. This enables complex feedback effects, such as flammagenitus clouds that arise from smoke and convective uplift and can subsequently lead to condensation and rain capable of extinguishing fires at other sources.…”

Section: Related Workmentioning

confidence: 99%

Volcanic Skies: coupling explosive eruptions with atmospheric simulation to create consistent skyscapes

Pretorius,

Gain,

Lastic

et al. 2024

Computer Graphics Forum

View full text Add to dashboard Cite

Explosive volcanic eruptions rank among the most terrifying natural phenomena, and are thus frequently depicted in films, games, and other media, usually with a bespoke once‐off solution. In this paper, we introduce the first general‐purpose model for bi‐directional interaction between the atmosphere and a volcano plume. In line with recent interactive volcano models, we approximate the plume dynamics with Lagrangian disks and spheres and the atmosphere with sparse layers of 2D Eulerian grids, enabling us to focus on the transfer of physical quantities such as temperature, ash, moisture, and wind velocity between these sub‐models. We subsequently generate volumetric animations by noise‐based procedural upsampling keyed to aspects of advection, convection, moisture, and ash content to generate a fully‐realized volcanic skyscape. Our model captures most of the visually salient features emerging from volcano‐sky interaction, such as windswept plumes, enmeshed cap, bell and skirt clouds, shockwave effects, ash rain, and sheathes of lightning visible in the dark.

show abstract

“…This separation between simulation and model realization is motivated primarily by memory and computational efficiency considerations. It stands in contrast to a branch-module strategy [Hädrich et al 2021;Makowski et al 2019;Palubicki et al 2022], which uses tree geometry during simulation, albeit using an abstraction of secondary branches and their subsidiaries that supports GPU instancing.…”

Section: Enhanced Simulationmentioning

confidence: 99%

DeadWood: Including Disturbance and Decay in the Depiction of Digital Nature

Peytavie,

Gain,

Guérin

et al. 2024

ACM Trans. Graph.

View full text Add to dashboard Cite

The creation of truly believable simulated natural environments remains an unsolved problem in Computer Graphics. This is, in part, due to a lack of visual variety. In nature, apart from variation due to abiotic and biotic growth factors, a significant role is played by disturbance events, such as fires, windstorms, disease, and death and decay processes, which give rise to both standing dead trees (snags) and downed woody debris (logs). For instance, snags constitute on average \(10\% \) of unmanaged forests by basal area, and logs account for \(2 \frac{1}{2} \) times this quantity. While previous systems have incorporated individual elements of disturbance (e.g., forest fires) and decay (e.g., the formation of humus), there has been no unifying treatment, perhaps because of the challenge of matching simulation results with generated geometric models. In this paper, we present a framework that combines an ecosystem simulation, which explicitly incorporates disturbance events and decay processes, with a model realization process, which balances the uniqueness arising from life history with the need for instancing due to memory constraints. We tested our hypothesis concerning the visual impact of disturbance and decay with a two-alternative forced-choice experiment ( n = 116). Our findings are that the presence of dead wood in various forms, as snags or logs, significantly improves the believability of natural scenes, while, surprisingly, general variation in the number of model instances, with up to 8 models per species, and a focus on disturbance events, does not.

show abstract

Fire in paradise

Cited by 4 publications

References 100 publications

SWIFT: Simulated Wildfire Images for Fast Training Dataset

SWIFT: Simulated Wildfire Images for Fast Training Dataset

Volcanic Skies: coupling explosive eruptions with atmospheric simulation to create consistent skyscapes

DeadWood: Including Disturbance and Decay in the Depiction of Digital Nature

Contact Info

Product

Resources

About