Real-time fire segmentation via Active Contours for UAV integrated wildfire propagation prediction

Francesco De Vivo, Manuela Battipede, Piero Gili, Anthony Yezzi, Eric Feron, Eric Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Accurate wildfire spread prediction is a key element in planning effective ground and aerial operations. Because of the underlying complex dynamic multi-physics processes driving the forest fire phenomena and the high number of parameters involved, finding an analytical solution is a challenging task. Current operational wildfire spread simulators, used by national governmental agencies are FARSITE, PROMETHEUS, PHOENIX RapidFire. These tools are based on empirical models developed and tuned using laboratory and historical wildfire data. This aspect makes the solution provided by these simulators inaccurate over long periods of time. To overcome these limitations, a closed loop architecture, where real time field measurements are fed back into the system, is the most promising solution. In this scenario, the use of an unmanned platform considerably reduces the risk associated with flying a manned aircraft in a low visibility and extremely turbulent air and improves the on-board Electro-Optical (EO) sensor effectiveness by flying at very low altitudes. In this paper a robust fire segmentation algorithm for wildfire front tracking is presented. This algorithm is based on the solution of Partial Differential Equations (PDE) to model a time evolving curve. An efficient implementation of the Level Set method enables the algorithm to fulfil real time requirements. Flight tests over a prescribed burn have been carried out to collect real data about the fire dynamics and to validate the algorithm and to test its robustness.
Original languageEnglish (US)
Title of host publicationAIAA Information Systems-AIAA Infotech at Aerospace, 2018
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105272
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

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