BDD-Net+: A Building Damage Detection Framework Based on Modified Coat-Net

Abstract

An accurate and fast assessment of damaged buildings following a disaster is critical for planning rescue and reconstruction efforts. The damage assessment by traditional methods is time-consuming and with limited performance. In this paper, we propose an end-to-end deep-learning network named Building Damage Detection Network-plus (BDD-Net+). The BDD-Net+ is based on a combination of convolution layers and transformer blocks. The proposed framework takes advantage of the multiscale residual convolution blocks and self-attention layers. The proposed framework consists of four main steps: (1) data preparation, (2) model training, (3) damage map generation and evaluation, and (4) the use of an explainable artificial intelligence (XAI) framework for understanding and interpretation of the operation model. Experimental results include two representative real-world benchmark datasets (i.e., the Haiti earthquake and the Bata explosion). The obtained results illustrate that BDD-Net+ achieves excellent efficacy in comparison with other state-of-the-art methods. Furthermore, the visualization of the results by XAI shows that BDD-Net+ provides more interpretable and explainable results for damage detection than other studied methods.