An Application Framework for Forest Fire and Haze Detection with Data Acquisition Using Unmanned Aerial Vehicle

Saadat, Md. Nazmus; Husen, Mohd Nizam

doi:10.1145/3164541.3164624

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…It can also be seen that the distribution was relatively equitable between the two remaining: case study (37.5%) and experiment (26.3%). [29,45,145,181,[190][191][192][193][194][195] It was observed that the most widely used type of DSS continued to be the traditional PDSS (including GIS). Despite technological advances, with the application of AI and Big Data, there is low use of these in the field of forest fires.…”

Section: Rq3mentioning

confidence: 99%

“…. MDPI 2018 [192] An application framework for forest fire and haze detection with data acquisition using unmanned aerial vehicle Saadat M.N., Husen M.N. ACM 2018 [193] Big data analysis in UAV surveillance for wildfire prevention and management Athanasis N, Themistocleous M, Kalabokidis K, .…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…BA[77,86,90,149,172,[180][181][182][183][184][185][186][187][188][189] Big Data[29,45,145,181,[190][191][192][193][194][195] Figure6. Number of studies found versus the problem to be solved.…”

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confidence: 99%

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Decision Support System Development of Wildland Fire: A Systematic Mapping

Vásquez

Cravero

Castro

et al. 2021

Forests

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Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last decade, different technologies have been applied to fight them. Selecting a specific technology and Decision Support Systems (DSS) is fundamental, since the results and validity of this could drastically oscillate according to the different environmental and geographic factors of the terrain to be studied. Given the above, a systematic mapping was realized, with the purpose of recognizing the most-used DSS and context where they have been applied. One hundred and eighty-three studies were found that used different types of DSS to solve problems of detection, prediction, prevention, monitoring, simulation, administration, and access to routes. The concepts key to the type of solution are related to the use or development of systems or Information and Communication Technologies (ICT) in the computer science area. Although the use of BA and Big Data has increased in recent years, there are still many challenges to face, such as staff training, the friendly environment of DSS, and real-time decision-making.

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Section: Rq3mentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

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Decision Support System Development of Wildland Fire: A Systematic Mapping

Vásquez

Cravero

Castro

et al. 2021

Forests

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“…It should be noted here that the drones category includes all vehicles that have more than one propeller mounted horizontally to the main body of the vehicle. More specifically, there were ten applications with drones: seven with quadcopters [19][20][21][22][23][24][25], three applications with hexacopter [15,26,27] and one with octacopter [28]. In addition, there were four applications with fixed-wing UAVs [29][30][31][32], while with single-rotor, it was limited to one application [33].…”

Section: Research Early Statisticsmentioning

confidence: 99%

Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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“…Además, han servido de ayuda para el cuidado de zonas explotadas y tratadas para la extracción del petróleo, permitiendo una vigilancia en sus procesos de reforestación [36,37]. Los UAV han facilitado el desarrollando de dispositivos para la supervisión y control de incendios forestales, control de desastres naturales y peligros sobre acantilados rocosos, empleando técnicas de adquisición y procesamiento de imágenes multiespectrales, detección de monóxido y dióxido de carbono aplicando aprendizaje autónomo supervisado e implementación de Big data para la detección de la neblina o incendios en una etapa temprana [38,39].…”

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Vehículos aéreos no tripulados como alternativa de solución a los retos de innovación en diferentes campos de aplicación: una revisión de la literatura

Pedraza

Sarmiento

Vergel

et al. 2021

Investigación e Innovación en Ingenierías

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Objetivo: Identificar los principales campos de aplicación en los que los vehículos aéreos no tripulados son una alternativa de solución innovadora, así como las tecnologías implementadas en ellos, articulando información fragmentada. Metodología: Mediante la herramienta Tree of Science se desarrolló una búsqueda inicial en Web of Science y con base en la información obtenida se complementó con el modelo de revisión sistemática tomado como referencia las bases de datos Scopus, ScienceDirec, IEEE y Web of Science aplicando una selección y descarte de información de las diferentes investigaciones relacionadas. Resultados y Conclusiones: Se seleccionaron un total de 81 artículos que fueron analizados y estudiados acorde a las aplicaciones de enfoque donde los vehículos aéreos no tripulados han presentado aportes, soluciones y alternativas a las labores diarias implementadas en áreas como agricultura, ambiente, militar, logística, mapeo y geociencias. Los vehículos aéreos más utilizados como alternativa de solución son los multirrotor que obtuvieron un 58.82 % de participación, seguido de los ala fija o flexible con un 31.76 %. Además, se encontró que el 91.36 % adaptan cámaras en estas aeronaves para el desarrollo de sus estudios. Finalmente, se observó que el área de la logística, en términos generales, es un tema potencial para estudios futuros.

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An Application Framework for Forest Fire and Haze Detection with Data Acquisition Using Unmanned Aerial Vehicle

Cited by 7 publications

References 4 publications

Decision Support System Development of Wildland Fire: A Systematic Mapping

Decision Support System Development of Wildland Fire: A Systematic Mapping

Computer Vision for Fire Detection on UAVs—From Software to Hardware

Vehículos aéreos no tripulados como alternativa de solución a los retos de innovación en diferentes campos de aplicación: una revisión de la literatura

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