An Attention-Based System for Damage Assessment Using Satellite Imagery
This work addresses the need for fast and accurate damage assessment for emergency responders, but it is incremental as it builds on existing deep learning methods with an attention mechanism.
The paper tackles the problem of automatically assessing building damage from satellite imagery before and after disasters, presenting a model that achieves accurate damage classification and segmentation on the xView2 dataset.
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.