Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation

The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To the best of our knowledge, there are a limited number of published studies in the literature, which address the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. As such, in this paper, we present an up-to-date and comprehensive review and analysis of these vision methods and their performances. First, previous works related to wildland fire classification, detection, and segmentation based on deep learning including vision transformers are reviewed. Then, the most popular and public datasets used for these tasks are presented. Finally, this review discusses the challenges present in existing works. Our analysis shows how deep learning approaches outperform traditional machine learning methods and can significantly improve the performance in detecting, segmenting, and classifying wildfires. In addition, we present the main research gaps and future directions for researchers to develop more accurate models in these fields.

Automated summary

In recent years, there has been a rise in wildland fires worldwide due to various factors. Climate change is expected to be the main driver for the increasing number of these fires in the coming years. The development of remote fire detection systems based on deep learning models and vision transformers shows promising solutions for addressing this issue. However, there is a lack of published studies on the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. This paper provides a comprehensive review and analysis of these vision methods and their performances, highlighting the superiority of deep learning approaches over traditional machine learning methods and discussing the research gaps and future directions in this field.