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Flood Detection using Deep Learning

The recent years have seen an unprecedented soar in floods worldwide, especially in Kerala. These floods cause massive economic damages to governments and individuals and private institutions like insurance companies. For this reason, new researches are set up to find new methods to serve the community and those entities better themselves to improve services provided when this kind of phenomenon occurs for better crisis response and mitigation. The proposed method mainly consists of two parts, flood detection and flood depth detection. The two modules work in parallel to reduce the computation time. The flood detection module detects floods from the images that are acquired from social media and other sources. For this purpose, a MobileNet is used as the backbone network. The depth detection is performed on the same dataset using Mask RCNN and VGG16. Feature maps are created from the image, which makes easier handling in the other modules. Then it is given to the RPN, where it provides scores for each object in the image. These are known as the Region of Interest (ROI). Mask R-CNN helps to create a bounded box and also a mask on the object based on the RoI.