Automatic Building Damage Assessment Using Deep Learning and Ground-Level Image Data

We propose a novel damage assessment deep model for buildings. Common damage assessment approaches require both pre-event and post-event data, which are not available in many cases, to classify damaged areas based on the severity of destruction. In this work, we focus on assessing damage to buildings using only post-disaster data in a continuous fashion. Our model utilizes three different neural networks, one network for pre-processing the input data and two networks for extracting deep features from the input source. Combinations of these networks are distributed among three separate feature streams. A regressor summarizes extracted features into a single continuous value denoting the destruction level. To evaluate the model, we collected a small dataset of ground-level image data of damaged buildings. Experimental results demonstrate that models taking advantage of hierarchical rich features outperform baseline methods.