Publications

Abstract : In recent times, frequent occurrences of natural disasters have been the cause of widespread disruptions to life and property. Albeit attempts to prevent such disasters may be a lost cause, emerging technologies can be resorted to, for minimization of their impact. This study proposes a deep learning-based computer vision and crowdsourcing methodology for the detection and estimation of flood depths, one of the most intense disruptive disasters. State-of-the-art flood detection systems work off of satellite or radar images. This research deals with processing images, captured at random, from flood ravaged zones, by smartphones or digital cameras. The crowdsourced image collection of the flood scenes afford better coverage and diverse perspectives, for assessments of the flood devastation. This paper proffers a fuzzy logic-based algorithm, and image segmentation based on color, to estimate the extent of flooding by analysis of crowdsourced images. Deployment of these methods helps in classification of the flooded areas into high, medium, or low level of flooding, to facilitate cost-effective, time-critical rescue operations. This algorithm yielded an accuracy of 83.1% on our dataset.