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When disaster strikes, accurate situational information and a fast, effectiveresponse are critical to save lives. Widely available, high resolutionsatellite images enable emergency responders to estimate locations, causes, andseverity of damage. Quickly and accurately analyzing the extensive amount ofsatellite imagery available, though, requires an automatic approach. In thispaper, we present Siam-U-Net-Attn model - a multi-class deep learning modelwith an attention mechanism - to assess damage levels of buildings given a pairof satellite images depicting a scene before and after a disaster. We evaluatethe proposed method on xView2, a large-scale building damage assessmentdataset, and demonstrate that the proposed approach achieves accurate damagescale classification and building segmentation results simultaneously.

An Attention-Based System for Damage Assessment Using Satellite Imagery