Multi-class segmentation of urban floods from multispectral imagery using deep learning

Natural disasters such as floods, earthquakes, hurricanes, etc. have a huge impact on a society—causing destruction of life and property in their wake. During disasters such as flood, it is crucial to understand the dynamics of the situation as it occurs for effective response. In this paper, we address the problem of satellite image classification for urban floods using deep learning. We propose an encoder-decoder neural network based on the Efficient Residual Factorized Convnet(ERFNet), for multi-class segmentation of urban floods from multi-spectral satellite imagery. The ERFNet architecture capitalizes on skip connections and one dimensional convolutions to achieve the best possible trade-off between accuracy and efficiency. Since time is of essence during a disaster, the choice of the ERFNet architecture on a high performance computing (HPC) platform is apt. Satellite imagery from WorldView-2 of floods in Srinagar, India during September 2014 have been used for this study. The tool ‘markGT’ has been developed to assist end-to-end annotation of satellite imagery. The urban flood dataset used for this study has been generated using markGT. The proposed deep learning model over urban flood satellite imagery gives promising results on Nvidia Tesla K80 GPU. We envisage that the proposed model could be extended and improved for real-time classification of urban floods, thereby aiding disaster response personnel in making informed decisions.