Rapid response mapping of floodwater extents in urbanized areas, while essential for early damage assessment and rescue operations, also presents significant image interpretation challenges. Images from visible band (red–green–blue (RGB)) remote sensors are the most common and cost-effective for real-time applications. Based on an understanding of the differing characteristics of turbid floodwater and urban land surface classes, a robust method was developed and automatized to extract visible floodwater using RGB band digital numbers. The methodology was applied to delineate visible floodwater distribution from very high-resolution aerial image data acquired during the 2013 Calgary flood event. The methodology development involved segment- and pixel-based feature analysis, rule development, automated feature extraction, and result validation processing. The accuracies for the visible floodwater class were above 0.8394% and the overall accuracies were above 0.9668% at both pixel and segment levels for three test sites with diverse urban landscapes.
Automated Extraction of Visible Floodwater in Dense Urban Areas from RGB Aerial Photos
