Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior
This work addresses the need for improved fire and plume spread models to mitigate increasing wildfire severity, though it appears incremental as it combines existing techniques like image segmentation and graph theory.
The paper tackles the problem of accurately modeling wildfire and plume spread by introducing an approach that isolates and tracks fire and plume behavior across multiple scales and image types, effectively distinguishing them from visually similar objects and enabling dataset generation for machine learning models.
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.