Early Forest Fire Detection Using Drones and Artificial Intelligence

Kinaneva, Diyana; Hristov, Georgi; Raychev, Jordan; Zahariev, Plamen

doi:10.23919/mipro.2019.8756696

Cited by 131 publications

(66 citation statements)

References 3 publications

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“…For improved presentation, the extracted forest fire detections are joined with landscape information and meteorological data. In [ 80 ] two types of UAVs, a fixed-wing drone and a rotary-wing drone equipped with optical and thermal cameras were used. As soon as the fixed-wing drone detects a fire, the rotary-wing drone will fly at a much lower altitude (10 to 350 m) compared to a fixed-wing UAV (350 to 5500 m), thus having better and more detailed visibility of the area and reducing false alarms through a neural network.…”

Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

284

106

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The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

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Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

284

106

View full text Add to dashboard Cite

show abstract

“…Besides, the results show that the use of intelligent sensors and IoT for real-time detection in applications like fire action (R15.4) has received lower attention. Contrary to other SDGs' recommendations related to IoT with very favourable results, this outcome is surprising since such a technology, even when still needs development to reach greater level of maturity, has demonstrated a strong efficiency and impact on fire detection [29][30][31] and land health monitoring [32][33][34]. Notwithstanding, it is important to remark that this result should be interpreted in terms of relative, pairwise preferences among the five candidate recommendations.…”

Section: Sdg 13: Climate Actionmentioning

confidence: 66%

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

et al. 2021

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The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

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“…Regarding the hardware, they use visible [26,27], thermal [28,29], multispectral [30,31] and infrared cameras [20,32], as well as environmental sensors (mostly used in indoor scenarios [33], but also proposed for forests [21]). Regarding the software, traditional computer vision algorithms [22,34] compete with recent artificial intelligence solutions [35,36]. The most common features used to recognize fires in aerial images are color, geometry, and movement.…”

Section: Surveillancementioning

confidence: 99%

“…Additionally, UGVs are used as base stations for UAVs, centralizing the communications between the fleet, processing the data collected by them, and coordinate their tasks in the scenario. Moreover, the work published in [35] proposes the use of two different types of drones: fixed-wing UAVs for medium-altitude flights searching fires and rotary-wing UAVs for low-altitude flights checking detections. The need for checking detection to avoid false alarms is also expressed in [39], which suggests the use of multiple drones to collect simultaneous information of every area, as well as the use of various features to detect fires in the provided images (e.g., color and movement).…”

Section: Surveillancementioning

confidence: 99%

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

2021

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Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm.

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Early Forest Fire Detection Using Drones and Artificial Intelligence

Cited by 131 publications

References 3 publications

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

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