2021
DOI: 10.3390/drones5040130
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UAV Patrolling for Wildfire Monitoring by a Dynamic Voronoi Tessellation on Satellite Data

Abstract: Fire monitoring and early detection are critical tasks in which Unmanned Aerial Vehicles (UAVs) are commonly employed. This paper presents a system to plan the drone patrolling schedule according to a real-time estimation of a fire propagation index that is derived from satellite data, such as the Normalized Difference Vegetation Index (NDVI) measurement and the Digital Elevation Model (DEM) of the surveilled area. The proposed system employs a waypoint scheduling logic, derived from a dynamic Voronoi Tessella… Show more

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Cited by 21 publications
(5 citation statements)
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“…Various studies have demonstrated the effectiveness of machine learning and deep neural networks for the evaluation of various safety-related risk factors, such as fire risk [6], [7] and landslide susceptibility [8], [9]. For instance, logistic regression was used by numerous researchers as the authors of [8] to show how, by considering influencing factors like slope, lithology, land cover, aspect, hill-shade, it is possible to evaluate LSMs in various regions such as China [9] and Sri Lanka [10].…”
Section: Related Workmentioning
confidence: 99%
“…Various studies have demonstrated the effectiveness of machine learning and deep neural networks for the evaluation of various safety-related risk factors, such as fire risk [6], [7] and landslide susceptibility [8], [9]. For instance, logistic regression was used by numerous researchers as the authors of [8] to show how, by considering influencing factors like slope, lithology, land cover, aspect, hill-shade, it is possible to evaluate LSMs in various regions such as China [9] and Sri Lanka [10].…”
Section: Related Workmentioning
confidence: 99%
“…Nevertheless, we needed a "good enough" model to simulate a credible wildfire. To that end, we created a simple cellular automaton [58][59][60][61] based on the same principles as those underpinning the widely used "Prometheus" model [46]. In the interest of transparency, the world representation and transition rules governing this automaton are described below.…”
Section: Motivating the Scope Of The Modelmentioning
confidence: 99%
“…In the literature, fire models are often based on cellular automata [58][59][60][61] and our model follows this direction: we start from a hexagonal grid in which every cell has 6 equidistant neighbours (as opposed other setups, such as 4 in the so-called von Neumann neighbourhood or 8 in the Moore neighbourhood [58]) representing the forest. Each cell is characterised by several variables: its position in the mesh (x and y coordinates), the amount of fuel it contains before the start of the fire event (fuel 0 , a real number between 0 and 1), the current amount of fuel (which progressively decreases when the cell is on fire) and its state, which can be "intact", "burning" or "burnt".…”
Section: Environment Topology Fuel and The Depletion Of Fuel Due To Firementioning
confidence: 99%
See 1 more Smart Citation
“…In this case, a research study developed a system for the patrolling planning of UAVs to monitor fires in forests. To this purpose, researchers applied a route point scheduling logic combining features of the region with real-time measurements [42]. Another idea to detect a fire at the early stage is to use the animal's body in the forest to deploy IoT-based sensors.…”
Section: Drone Technologymentioning
confidence: 99%