  When disaster strikes, accurate situational information and a fast, effective
response are critical to save lives. Widely available, high resolution
satellite images enable emergency responders to estimate locations, causes, and
severity of damage. Quickly and accurately analyzing the extensive amount of
satellite imagery available, though, requires an automatic approach. In this
paper, we present Siam-U-Net-Attn model - a multi-class deep learning model
with an attention mechanism - to assess damage levels of buildings given a pair
of satellite images depicting a scene before and after a disaster. We evaluate
the proposed method on xView2, a large-scale building damage assessment
dataset, and demonstrate that the proposed approach achieves accurate damage
scale classification and building segmentation results simultaneously.
