Building Damage Assessment Using Feature Concatenated Siamese Neural Network

Abstract

Fast and accurate post-earthquake building damage assessment is an important task to do to define search and rescue procedures. Many approaches have been proposed to automate this process by using artificial intelligence, some of which use handcrafted features that are considered inefficient. This research proposed end-to-end building damage assessment based on a Siamese neural network. We modify the network by adding a feature concatenation mechanism to enrich the data feature. This concatenation mechanism creates different features based on each output from the convolution block. It concatenates them into a high-dimensional vector so that the feature representation is more likely to be linearly separable, resulting in better discrimination capability than the standard siamese. Our model was evaluated through three experimental scenarios where we performed classification of G1 or G5, G1-G4 or G5, and all the five grades of EMS-98 building damage description. Our models are superior to the standard Siamese neural network and state-of-the-art in this field. Our model obtains f1-scores of 79.47%, 54.09%, 40.64% and accuracy scores of 87.24%, 95.28%, and 42.57% for the first, second, and third experiments, respectively.

Keywords

• Classification
• deep learning
• disaster
• earthquake
• LiDAR
• siamese neural network