Better prediction and monitoring of flood events are key factors contributing to the reduction
of their impact on local communities and infrastructure assets. Flood management involves …

Precise monitoring of floods is significant in disaster management and loss reduction;
however, remote sensing data resource and methods can largely affect the monitoring …

The interest in visual-based surveillance systems, especially in natural disaster applications,
such as flood detection and monitoring, has increased due to the blooming of surveillance …

Floods are among the most common natural hazards in urban areas. To mitigate the
problems caused by flooding, unstructured data such as images and videos collected from …

In recent years, social media platforms such as Twitter have garnered a lot of interest as a
new source of text data for quick flood awareness and effective prediction. Hence various …

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic
segmentation of flood images. Imagery datasets of urban flooding were used to train two …

The application of unmanned aerial vehicle (UAV) used to capture the images of the flood
areas are becoming interest of most researchers recently. This is due to its versatilities of …

In smart cities, citizens contribute to improving the overall quality of life through infrastructure
deficiency signaling. Multimedia content (images, videos) uploaded using smartphones …

A novel method is proposed to automatically segment water extent using optical data. The
key features of this approach are (i) the development of a simple physically based model …

The impact of floods is the most severe among the natural calamities occurring in Malaysia.
The knock of floods is consistent and annually forces thousands of Malaysians to relocate …