  Detecting roadway segments inundated due to floodwater has important
applications for vehicle routing and traffic management decisions. This paper
proposes a set of algorithms to automatically detect floodwater that may be
present in an image captured by mobile phones or other types of optical
cameras. For this purpose, image classification and flood area segmentation
methods are developed. For the classification task, we used Local Binary
Patterns (LBP), Histogram of Oriented Gradients (HOG) and pre-trained deep
neural 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 all
other methods. For the flood area segmentation task, we investigated superpixel
based methods and Fully Convolutional Neural Network (FCN). Similar to the
classification task, we trained logistic regression and k-nearest neighbors
classifiers on the superpixel areas and compared that with an end-to-end
trained FCN. Conditional Random Fields (CRF) method was applied after both
segmentation methods to post-process coarse segmentation results. FCN offered
the highest scores in all metrics; it was followed by superpixel-based logistic
regression and then superpixel-based KNN.
