Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior

2024-08-16 · Daryn Sagel, Bryan Quaife

The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale ($10^4$-$10^5$ m) satellite images to sub-microscale ($10^0$-$10^1$ m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.

📄 PDF Abstract BibTeX arXiv:2408.08984

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quaife/FDV_Data 공식 구현

Tasks

Dataset GenerationImage Inpainting Image Segmentation Semantic Segmentation

Methods 이 논문이 사용한 방법론

Inpainting Train a convolutional neural network to generate the contents of an arbitrary image region conditioned on its surroundings.

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2026-01-06 · Chris Webb, Mobin Habibpour, Mayamin Hamid Raha, Ali Reza Tavakkoli 외 arxiv

Wildfire monitoring demands autonomous systems capable of reasoning under extreme visual degradation, rapidly evolving physical dynamics, and scarce real-world training data. Existing UAV navigation approaches rely on si…

Reinforcement Learning

Fire Detection Segmentation

Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior

Code (1)

Tasks

Methods 이 논문이 사용한 방법론

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