Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

• Published in: Smart cities (Basel) 2021-09, Vol.4 (3), p.1220-1242
• Main Authors: Munawar, Hafiz Suliman, Ullah, Fahim, Qayyum, Siddra, Heravi, Amirhossein
• Format: Article
• Language: English
• Subjects: Algorithms; Automatic vehicle identification systems; Buildings; Deep learning; Disaster management; Disaster relief; Disasters; Emergency preparedness; Emergency response; flood detection; Flood management; Flooding; Floods; Geographic information systems; Global positioning systems; GPS; Image classification; Internet; landmarks detection; Landslides & mudslides; Machine learning; Management systems; Natural disasters; Neural networks; Property damage; Real time; Roads; Satellite imagery; Satellites; Storm damage; UAV dataset; Unmanned aerial vehicles
• ISSN: 2624-6511; 2624-6511
• DOI: 10.3390/smartcities4030065

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.