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

TL;DR

This paper presents a novel image segmentation and tracking method for fire and plume behavior across multiple scales, aiding in wildfire modeling and validation.

Contribution

It introduces a combined approach using image segmentation and graph theory to isolate and track fire and plume dynamics across diverse image sources.

Findings

Successfully isolates fire and plume behavior across various image scales

Effectively distinguishes fires and plumes from similar objects

Leverages inpainting and dataset generation for modeling

Abstract

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.