PREDICTION OF FIRE SPREAD IN GRASSLANDS USING DEEP LEARNING TECHNIQUES

Fire metrics such as fire front location and rate of spread (ROS) are critical to understanding the behavior of prescribed fires and wildfires. This project proposes a new method for prescribed grass fire evolution mapping and ROS measurement using multi temporal thermal orthomosaics collected by a small fixed-wing Unmanned Aircraft System (UAS) at low altitudes. The proposed method provides a low-cost, safe, and effective solution for active grass fire monitoring and fire metric measurement in areas that may be challenging for a typical rotor-wing UAS to cover due to endurance and size constraints. ancient times grass have played an important role in social, economic, and religious activities and have enriched human life in a variety of ways both material and psychological. To protect our nature from these rapidly rising grass fires, we need to be cautious enough of every decision we take which could lead to a disastrous end, once and for all. This report is sent to the Admin department for further verification and validation. Finally, we compared the performance of our method with those of recently reported fire detection approaches employing widely used performance matrices to test the achieved fire classification results.