Fire Detection with Deep Learning: A Comprehensive Review

Rodrigo N. Vasconcelos (), Washington J. S. Franca Rocha, Diego P. Costa, Soltan G. Duverger, Mariana M. M. de Santana, Elaine C. B. Cambui, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa and Carlos Leandro Cordeiro
Additional contact information
Rodrigo N. Vasconcelos: Postgraduate Program in Earth Modeling and Environmental Sciences PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil
Washington J. S. Franca Rocha: Postgraduate Program in Earth Modeling and Environmental Sciences PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil
Diego P. Costa: GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
Soltan G. Duverger: GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
Mariana M. M. de Santana: Forest Engineering Institute (FEI/UEAP), State University of Amapá—UEAP, Av. Pres. Getúlio Vargas, 650 Centro, Macapá 68900-070, AP, Brazil
Elaine C. B. Cambui: Professional Master’s Degree in Applied Ecology, Institute of Biology, Federal University of Bahia—UFBA, Salvador 40170-115, BA, Brazil
Jefferson Ferreira-Ferreira: World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, SP, Brazil
Mariana Oliveira: World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, SP, Brazil
Leonardo da Silva Barbosa: World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, SP, Brazil
Carlos Leandro Cordeiro: World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, São Paulo 05422-030, SP, Brazil

Land, 2024, vol. 13, issue 10, 1-20

Abstract: Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the performance of wildfire detection systems. This paper provides a comprehensive review of fire detection using deep learning, spanning from 1990 to 2023. This study employed a comprehensive approach, combining bibliometric analysis, qualitative and quantitative methods, and systematic review techniques to examine the advancements in fire detection using deep learning in remote sensing. It unveils key trends in publication patterns, author collaborations, and thematic focuses, emphasizing the remarkable growth in fire detection using deep learning in remote sensing (FDDL) research, especially from the 2010s onward, fueled by advancements in computational power and remote sensing technologies. The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. Highly cited papers are explored, revealing their substantial influence on the field’s research focus. The analysis underscores the practical implications of integrating high-quality input data and advanced deep-learning techniques with remote sensing for effective fire detection. It provides actionable recommendations for future research, emphasizing interdisciplinary and international collaboration to propel FDDL technologies and applications. The study’s conclusions highlight the growing significance of FDDL technologies and the necessity for ongoing advancements in computational and remote sensing methodologies. The practical takeaway is clear: future research should prioritize enhancing the synergy between deep learning techniques and remote sensing technologies to develop more efficient and accurate fire detection systems, ultimately fostering groundbreaking innovations.

Keywords: deep learning; forest fire detection; forest fire; wildfire; wildfire detection (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
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