  Fast and effective responses are required when a natural disaster (e.g.,
earthquake, hurricane, etc.) strikes. Building damage assessment from satellite
imagery is critical before relief effort is deployed. With a pair of pre- and
post-disaster satellite images, building damage assessment aims at predicting
the extent of damage to buildings. With the powerful ability of feature
representation, deep neural networks have been successfully applied to building
damage assessment. Most existing works simply concatenate pre- and
post-disaster images as input of a deep neural network without considering
their correlations. In this paper, we propose a novel two-stage convolutional
neural network for Building Damage Assessment, called BDANet. In the first
stage, a U-Net is used to extract the locations of buildings. Then the network
weights from the first stage are shared in the second stage for building damage
assessment. In the second stage, a two-branch multi-scale U-Net is employed as
backbone, where pre- and post-disaster images are fed into the network
separately. A cross-directional attention module is proposed to explore the
correlations between pre- and post-disaster images. Moreover, CutMix data
augmentation is exploited to tackle the challenge of difficult classes. The
proposed method achieves state-of-the-art performance on a large-scale dataset
-- xBD. The code is available at
https://github.com/ShaneShen/BDANet-Building-Damage-Assessment.
