A Comprehensive Feature Extraction Network for Deep-Learning-Based Wildfire Detection in Remote Sensing Imagery  
[PERSON]  
1 College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; [EMAIL_ADDRESS]. Both MODIS and VIIRS are capable of detecting the thermal radiation signals emitted during wildfire incidents by utilizing thermal infrared bands and implementing threshold algorithms to identify fire locations [12; 14]. In recent years, the proliferation of artificial intelligence, particularly through the implementation of machine learning and deep learning techniques, has prompted a growing body of research focused on enhancing the accuracy and real-time capabilities of wildfire monitoring systems [15; 16; 17; 18; 19]. The evolution of machine learning technologies has led to an increasing number of investigations aimed at integrating these methodologies into wildfire detection efforts. Among the pioneering machine learning algorithms, those utilized for remote sensing image classification are Support Vector Machines (SVM) and Random Forest (RF) [17; 20; 21]. In the context of wildfire detection, these algorithms are employed to construct classifiers that discern fire-related features within remote sensing imagery. SVM operates by identifying the optimal hyperplane for classifying image data, demonstrating robust generalization capabilities, particularly in the analysis of nonlinear datasets [22]. Conversely, Random Forest employs a voting mechanism derived from multiple decision trees, which allows it to effectively navigate the intricate relationships among various features [23]. Nonetheless, traditional machine learning approaches encounter challenges when addressing complex spatiotemporal data. For instance, in the analysis of multi-dimensional datasets, conventional algorithms may inadequately capture the comprehensive information from fire points. As advancements in deep learning technologies continue to accelerate, an increasing number of studies are transitioning towards the adoption of deep learning methodologies. The effective utilization of Convolutional Neural Networks (CNNs) in image processing has established them as a prominent deep learning technique for wildfire monitoring [24]. CNNs have the ability to autonomously learn features from images and to derive increasingly abstract representations through multiple convolutional and pooling layers, which is particularly advantageous for fire detection in remote sensing imagery [15; 25; 26]. While CNNs primarily concentrate on spatial information, Recurrent Neural Networks (RNNs) excel at handling time-series data, thereby capturing temporal dependencies that are crucial for monitoring wildfires [27]. The manifestation of wildfires is frequently associated with a sequence of continuous changes, such as increasing temperatures and the dispersal of smoke. RNNs are capable of identifying these temporal patterns by retaining information from preceding time points. Fire detection methodologies that employ RNNs typically process satellite images in a chronological sequence, enabling the model to forecast the likelihood of fire occurrence at subsequent time intervals based on data from prior moments [28; 29]. In addition to remote sensing imagery, the monitoring of wildfires can benefit from the incorporation of diverse multisource data, including meteorological and geographic information. Research in this area often employs various data types as inputs for machine learning models, thereby enhancing both detection accuracy and robustness. For instance, combining remote sensing data with meteorological factors like temperature, humidity, and wind speed has been shown to significantly improve fire prediction accuracy, particularly under complex climatic conditions [25; 30]. These approaches typically utilize multimodal learning frameworks grounded in deep learning, which amalgamate information from multiple sources, consequently augmenting the overall efficacy of wildfire monitoring systems.