THEASIS System for Early Detection of Wildfires in Greece: Preliminary Results from its Laboratory and Small Scale Tests

Aspragathos, Nikos Α.; Sartinas, Evangelos; Katsaros, Dimitrios; Michos, Stefanos; Kanavos, Anastasios; Moulianitis, Vassilis C.; Dogkas, E.; Koutmos, P.; Paterakis, Georgios; Souflas, Konstantinos; Thanellas, Georgios; Xanthopoulos, Nikitas; Lamprinou, Nefeli; Psarakis, Emmanouil Ζ.

doi:10.1109/eaeeie46886.2019.9000445

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…In terms of APIs, in addition to OpenGL, TensorFlow Object Detection was used in four applications [29,30,34,58] and the OpenCV library in two applications [29,50]. Of particular interest is the THEASIS system which is a stand-alone proposed platform for early detection of big wildfires and was implemented in three applications [22,30,34].…”

Section: Software/methodsmentioning

confidence: 99%

“…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%

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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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Section: Software/methodsmentioning

confidence: 99%

Section: Research Early Statisticsmentioning

confidence: 99%

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

2021

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Communication Perspective of Wildfire Detection and Suppression: A Survey of Technologies, Requirements, and Future Directions

Canello,

Mikhaylov,

Hänninen

2023

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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Path Planning of Multiple UAVs for the Early Detection of Wildfires in the National Park of Kotychi and Strofylia Wetlands

Kritikou,

Xofis,

Souflas

et al. 2024

Fire

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The surveillance of the National Park Kotychi and Strofylia Wetlands in southwest Greece with Unmanned Aerial Vehicles (UAVs) is studied in this work. As comprehensive coverage of the region cannot be attained with just stationary ground cameras, multiple parallel moving UAVs are utilized. The region is divided into squares, which are further subdivided into regular grids with nodes whose weights are calculated based on the fire risk of the corresponding region. Heuristic methods are proposed for selecting the UAVs’ start and goal graph nodes. The graph with the start and goal nodes serves as input to the A* algorithm, which computes offline short paths that direct the UAVs to cross areas with the highest fire risk. The number of UAVs is progressively increased as the coverage of the previous detections proves insufficient. The authors determine the number of UAVs needed for each section of the divided area. They also demonstrate that the UAVs can scan simultaneously without collisions, as each UAV follows a unique path inaccessible to the others. Finally, the presented computations and results show that the proposed method can effectively contribute to fire scanning in the area.

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THEASIS System for Early Detection of Wildfires in Greece: Preliminary Results from its Laboratory and Small Scale Tests

Cited by 3 publications

References 11 publications

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

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

Communication Perspective of Wildfire Detection and Suppression: A Survey of Technologies, Requirements, and Future Directions

Path Planning of Multiple UAVs for the Early Detection of Wildfires in the National Park of Kotychi and Strofylia Wetlands

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