A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms

• Published in: Signal processing Vol. 190; p. 108309
• Main Authors: Bouguettaya, Abdelmalek, Zarzour, Hafed, Taberkit, Amine Mohammed, Kechida, Ahmed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2022
• Subjects: Aerial images processing; Computer vision; Deep learning; Smoke detection system; Unmanned aerial vehicle; Wildfire detection system; Deep learning; Computer vision; Smoke detection system; Unmanned aerial vehicle; Wildfire detection system; Aerial images processing
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2021.108309

Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefighters’ lives to danger. Thus, remote sensing technologies have become one of the most demanded strategies to fight against wildfires, especially UAV-based remote sensing technologies. They have been adopted to detect forest fires at their early stages, before becoming uncontrollable. Autonomous wildfire early detection from UAV-based visual data using different deep learning algorithms has attracted significant interest in the last few years. To this end, in this paper, we focused on wildfires detection at their early stages in forest and wildland areas, using deep learning-based computer vision algorithms to prevent and then reduce disastrous losses in terms of human lives and forest resources.