Analysis of Crowdsourced Images for Flooding Detection

Abstract

Crowdsourced images taken at near ground-level present a new source of data for real-time flooding detection. In this new study, crowdsourced images taken in the city of Norfolk, Virginia are analyzed to extract the inundated area. In the proposed analysis, images of the same flooded roads in the crowdsourced images are obtained under dry conditions for comparison and detection of the flooding. Few preprocessing steps are used to normalize the image sets with and without flooding that are then registered considering the image without flooding as reference. After the registration, an algorithm pipeline is developed to extract the flooded area in the crowdsourced images. The method accounts for reflection on the standing water due to nearby landmarks and overhead clouds/sky. First, the flooded area with reflections from nearby landmarks on the water is identified. Then, the algorithm uses the detected flooded area as a seed to detect the rest of the flooding with reflections from overhead clouds/sky by using the saturation channel in hue, saturation, and value (HSV) color model. The proposed algorithm detects the flooded area as described above since the reference images are not able to detect areas with reflection from overhead clouds/sky. The novelty of the proposed algorithm involves using a new source of data, crowdsourced images, and detecting flooded area with reflection of nearby landmarks and overhead clouds/sky. The proposed algorithm is tested on real images and quantitative evaluation (detection and discrimination accuracy), based on a ground truth, is also presented to evaluate the proposed algorithm.