UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

Hafiz Suliman Munawar, Fahim Ullah, Siddra Qayyum, Sara Imran Khan and Mohammad Mojtahedi
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Hafiz Suliman Munawar: School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia
Fahim Ullah: School of Civil Engineering and Surveying, University of Southern Queensland, Springfield, QLD 4300, Australia
Siddra Qayyum: School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia
Sara Imran Khan: Faculty of Chemical Energy, University of New South Wales, Kensington, Sydney, NSW 2052, Australia
Mohammad Mojtahedi: School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia

Sustainability, 2021, vol. 13, issue 14, 1-22

Abstract: Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

Keywords: convolutional neural network (CNN); disaster management; aerial imagery; flood detection; unmanned aerial vehicles (UAVs) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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