Remote Sensing Techniques Shaping the Future of Wildfire Early Detection

Abstract

The global wildfire crisis requires comprehensive early detection strategies to reduce environmental, economic, and societal impacts. This review systematically evaluates remote sensing (RS) technologies employed in early wildfire detection, classifying them into satellite systems, aerial platforms, and ground‐based sensors. Subsequently, it critically examines detection methodologies, which include image classification, object detection, semantic segmentation, and emergent approaches utilizing large language models (LLMs). Analysis reveals that deep learning (DL) significantly improves detection accuracy; however, its effectiveness is limited by resolution constraints, especially in the context of detecting smaller fires. As an advanced extension of the DL paradigm, LLMs built upon transformer‐based architectures complement conventional DL methods by enabling contextual reasoning over multimodal inputs, thereby bridging the gap between raw visual feature extraction and high‐level semantic interpretation. In this regard, LLMs demonstrate considerable potential in synthesizing complex, heterogeneous datasets, thereby enhancing the integration of diverse remote sensing data. Despite these advancements, substantial challenges remain, including limitations in spatial and temporal resolution, environmental interference, and complexities associated with data processing. To mitigate these obstacles, prospective research endeavors should prioritize strengthening algorithmic robustness, refining data fusion methodologies, and establishing real‐time monitoring systems. By integrating existing methodologies and outlining key challenges, this study aims to guide future academic inquiry in enhancing wildfire risk mitigation strategies and improving early detection frameworks.