Forest fire surveillance systems: A review of deep learning methods

Area of Science:

• Computer Science
• Environmental Science
• Artificial Intelligence

Background:

• Forest fires cause significant economic, environmental, and societal damage.
• Effective forest fire detection systems are crucial for timely mitigation and response.
• Early detection systems leveraging advanced technology are essential for forest management.

Purpose of the Study:

• To critically review state-of-the-art forest fire detection systems utilizing deep learning (DL) methods.
• To analyze 37 research articles published between January 2018 and 2023 on DL for forest fire detection.
• To identify trends in data types, augmentation, and DL architectures for this application.

Main Methods:

• Systematic review and analysis of 37 research articles on deep learning for forest fire detection.
• In-depth analysis of data characteristics (images, videos), augmentation techniques, and DL model architectures.
• Evaluation of model performance using metrics such as accuracy, mean average precision (mAP), and F1-Score.

Main Results:

• Deep learning models applied to forest fire surveillance have demonstrated favorable outcomes.
• The majority of reviewed studies achieved accuracy rates exceeding 90% in forest fire detection.
• Identified key DL applications including classification, detection, and segmentation tasks.

Conclusions:

• Deep learning methods hold significant potential for improving forest fire detection capabilities.
• Further research can enhance efficacy through hyper-parameter optimization, satellite data integration, and advanced augmentation.
• Optimized DL models are crucial for effective forest fire management and mitigation strategies.