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Detecting roadway segments inundated due to floodwater has importantapplications for vehicle routing and traffic management decisions. This paperproposes a set of algorithms to automatically detect floodwater that may bepresent in an image captured by mobile phones or other types of opticalcameras. For this purpose, image classification and flood area segmentationmethods are developed. For the classification task, we used Local BinaryPatterns (LBP), Histogram of Oriented Gradients (HOG) and pre-trained deepneural network (VGG-16) as feature extractors and trained logistic regression,k-nearest neighbors, and decision tree classifiers on the extracted features.Pre-trained VGG-16 network with logistic regression classifier outperformed allother methods. For the flood area segmentation task, we investigated superpixelbased methods and Fully Convolutional Neural Network (FCN). Similar to theclassification task, we trained logistic regression and k-nearest neighborsclassifiers on the superpixel areas and compared that with an end-to-endtrained FCN. Conditional Random Fields (CRF) method was applied after bothsegmentation methods to post-process coarse segmentation results. FCN offeredthe highest scores in all metrics; it was followed by superpixel-based logisticregression and then superpixel-based KNN.

Detecting floodwater on roadways from image data with handcrafted features and deep transfer learning