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Efficient localization of forest-fires based Unmanned Aerial Vehicles (UAVs) represents valuable assessment. Due to the fast deployment of UAVs, it is practical to use them. For forest fire detection purposes, usually the area to explore is unknown, thus existing strategies use an automatic coverage exploration strategy. However, such approach is not efficient in terms of exploration time since the mission execution and achievement in an unknown environment that needs a strong vehicle decision and control. Based on this observation, we improved the localization mission by a decision-based strategy resulting from a probabilistic model based on the temperature in order to estimate the distance towards the forest-fire. The UAV optimizes its trajectory according to the state of the forest-fire knowledge by using a map to represent its knowledge and updates it at each step of its exploration. We show in this paper that our …