Application of deep learning on UAV based aerial images for flood inundation mapping

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 inundation mapping. Smart Cities, 4. pp. 1220-1242.

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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 (https://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: 22 Oct 2021 06:25
Last Modified: 11 Feb 2022 07:02
Uncontrolled Keywords: flood detection; deep learning; landmarks detection; UAV dataset; disaster management
Fields of Research (2008): 09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
12 Built Environment and Design > 1202 Building > 120201 Building Construction Management and Project Planning
Fields of Research (2020): 33 BUILT ENVIRONMENT AND DESIGN > 3302 Building > 330202 Building construction management and project planning
40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing
Identification Number or DOI: https://doi.org/10.3390/smartcities4030065
URI: http://eprints.usq.edu.au/id/eprint/43517

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