An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters


David Erickson (Ed.)

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Journal of Computer Science Applications and Information Technology





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Forest fires are increasing annually due to global warming. The cause of most forest fires are humans, followed by lightning, and winds enabling dry debris, such as small pieces of dry wood that rub against each other creating a spark. Forest fires are catastrophic events that annually claim lives, destroy property, kill domestic and wild animals, and destroy the trees and vegetation that provide oxygen, filter the water, food for animals, and playgrounds and shelter for humans and animals. Additionally, after fires burn a forest, rain water runs down the mountain forming mudslides that often times cause damages to land, houses, other building that are on their way, cars, and other property and also kill people, and animals. Therefore, forest fires are a disaster having negative effects on people, animals and flora in many ways. In this research paper we provide a solution to preventing forest fires by monitoring the forest using an autonomous UAS powered by solar panels, batteries and a brushless motor, equipped with two high quality cameras in the visual range an infrared camera and a LIDAR, capable of recognizing dry brash, people making camp fires, dead trees, and providing that and other relevant information via wireless transmission to the personnel designated to protect the forest. The intelligent system of the UAS is embedded into a Stratix-10 Intel-Altera FPGA that is capable of performing 29.5 teraflops sustained, and is capable of detecting forest fires, as well as providing real time advice to the fire fighters how to efficiently fight the fire. In addition to that the UAS has a router a transceiver and an antenna, enabling the firefighters to communicate with each other and with command and control when the fire destroys the ground antenna system.


AI, Neural networks, Signal processing, Sensor Networks.



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