Forest fire detection using neural networks, remote sensing, and Geographic information systems (GIS) is an effective approach to monitor and respond to forest fires. This integrated system combines the power of machine learning, satellite imagery, and spatial analysis to detect and track fire inciden...
The use of both CNN and RNN for feature extraction is proposed in this article for the first time in the literature of forest fire detection. The performance of the proposed system has been evaluated on two publicly available fire datasets鈥擬ivia lab dataset and Kaggle fire dataset. ...
Explore and run machine learning code with Kaggle Notebooks | Using data from Forest Fires Data Set
The outcomes of the proposed approach are promising for the forest fire classification problem considering the unique forest fire detection dataset. Keywords: artificial intelligence; smart city application; forest fire classification; deep learning
IoT; AIoT; embedded ML; fire detection; audio signals; image signals; LoRaWAN1. Introduction Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. According to the European Forest Fire Information System [1], around 570570 ...
The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial ...
The outcomes of the proposed approach are promising for the forest fire classification problem considering the unique forest fire detection dataset. Keywords: artificial intelligence; smart city application; forest fire classification; deep learning
forest fire; fire detection; YOLOv8; deep learning; TranSDet; wildfire incidents; brushfire spread1. Introduction Forest fires are catastrophic events that result in widespread economic, ecological, and environmental damage all over the world. High temperatures can ignite dry fuels, such as sawdust,...
The proposed approach to the forest fire dataset shows superiority over the other methods. Table 5. Performance comparison of the proposed framework with previous fire detection methods. 5.5. Analysis and Discussion With the findings depicted in the earlier subsections, the suggested model shows the...
Forest fire classification utilizing attention mechanisms leverages advanced neural network techniques to efficiently identify and respond to critical patterns and features in imagery, enhancing the accuracy of fire detection and prevention [19,20]. Accordingly, we introduce an attention-guided multi-stream...