论文标题
使用卫星图像进行损害评估的基于注意力的系统
An Attention-Based System for Damage Assessment Using Satellite Imagery
论文作者
论文摘要
当灾难袭来，准确的情境信息和快速，有效的回应对于挽救生命至关重要。广泛可用的高分辨率卫星图像使应急人员能够估算损害的位置，原因和严重性。但是，快速准确地分析可用的大量卫星图像需要自动方法。在本文中，我们介绍了Siam-U-Net-ATTN模型（一种具有注意力机制的多级深度学习模型），以评估建筑物的损害水平，并在灾难之前和之后描绘一对卫星图像，描绘了场景。我们在XView2上评估了拟议的方法，这是一个大规模的建筑损害评估数据集，并证明所提出的方法同时实现了准确的损害量表分类和建筑细分结果。
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.
