A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis
Article
Article Title | A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis |
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ERA Journal ID | 212512 |
Article Category | Article |
Authors | Munawar, Hafiz Suliman (Author), Gharineiat, Zahra (Author), Akram, Junaid (Author) and Khan, Sara Imran (Author) |
Journal Title | Fire |
Journal Citation | 5 (4), pp. 1-15 |
Article Number | 122 |
Number of Pages | 15 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2571-6255 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/fire5040122 |
Web Address (URL) | https://www.mdpi.com/2571-6255/5/4/122 |
Abstract | The study aims to develop a holistic framework for maximum area coverage of a disaster region during a bushfire event. The monitoring and detection of bushfires are essential to assess the extent of damage, its direction of spread, and action to be taken for its containment. Bushfires limit human’s access to gather data to understand the ground situation. Therefore, the application of Unmanned Aerial Vehicles (UAVs) could be a suitable and technically advanced approach to grasp the dynamics of fires and take measures to mitigate them. The study proposes an optimization model for a maximal area coverage of the fire-affected region. The advanced Artificial Bee Colony (ABC) algorithm will be applied to the swarm of drones to capture images and gather data vital for enhancing disaster response. The captured images will facilitate the development of burnt area maps, locating access points to the region, estimating damages, and preventing the further spread of fire. The proposed algorithm showed optimum responses for exploration, exploitation, and estimation of the maximum height of the drones for the coverage of wildfires and it outperformed the benchmarking algorithm. The results showed that area coverage of the affected region was directly proportional to drone height. At a maximum drone height of 121 m, the area coverage was improved by 30%. These results further led to a proposed framework for bushfire relief and rescue missions. The framework is grounded on the ABC algorithm and requires the coordination of the State Emergency Services (SES) for quick and efficient disaster response. |
Keywords | bushfires; burnt area; damage detection; UAVs; ABC algorithm; evacuation |
ANZSRC Field of Research 2020 | 350703. Disaster and emergency management |
401304. Photogrammetry and remote sensing | |
461199. Machine learning not elsewhere classified | |
Byline Affiliations | School of Surveying and Built Environment |
University of Sydney | |
University of New South Wales | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7v53/a-framework-for-burnt-area-mapping-and-evacuation-problem-using-aerial-imagery-analysis
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