Application of deep learning on UAV-based aerial images for flood detection

Munawar, Hafiz Suliman and Ullah, Fahim ORCID: https://orcid.org/0000-0002-6221-1175 and Qayyum, Siddra and Heravi, Amirhossein ORCID: https://orcid.org/0000-0003-2939-7745 (2021) Application of deep learning on UAV-based aerial images for flood detection. Smart Cities, 4 (3). pp. 1220-1243. ISSN 2514-605X

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Abstract

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons. org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021)
Date Deposited: 23 Sep 2021 00:25
Last Modified: 08 Sep 2022 04:42
Uncontrolled Keywords: flood detection; deep learning; landmarks detection; UAV dataset; disaster management
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
12 Built Environment and Design > 1205 Urban and Regional Planning > 120504 Land Use and Environmental Planning
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
12 Built Environment and Design > 1205 Urban and Regional Planning > 120599 Urban and Regional Planning not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
33 BUILT ENVIRONMENT AND DESIGN > 3304 Urban and regional planning > 330499 Urban and regional planning not elsewhere classified
33 BUILT ENVIRONMENT AND DESIGN > 3304 Urban and regional planning > 330404 Land use and environmental planning
Identification Number or DOI: https://doi.org/10.3390/smartcities4030065
URI: http://eprints.usq.edu.au/id/eprint/43681

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