An Attention-Based System for Damage Assessment Using Satellite Imagery
Hanxiang Hao, Sriram Baireddy, Emily R. Bartusiak, Latisha Konz, Kevin J. LaTourette, Michael Gribbons, Moses W. Chan, Edward J. Delp, Mary L. Comer
Abstract
When a disaster strikes, accurate situational information and a fast, effective response are critical to save lives. High resolution satellite images enable emergency responders to estimate the location, cause, and severity of damage. In this paper, we present a Siam-U-Net-Attn model with an attention mechanism to assess the damage level of buildings given a pair of satellite images showing a scene before and after a disaster. We evaluate the proposed method on xView2, a building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation.