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 utilize both pre-event and post-event data, which are not available in many cases. In this work, we focus on assessing damage to buildings using only post-disaster. We estimate severity of destruction via 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 the 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.