Recognizing dynamic variations on the ground, especially changes caused by various natural disasters, is critical for assessing the severity of the damage and directing the disaster response. However, current workflows for disaster assessment usually require human analysts to observe and identify damaged buildings, which is labor-intensive and unsuitable for large-scale disaster areas. In this paper, we propose a difference-aware attention network (D2ANet) for simultaneous building localization and multi-level change detection from the dual-temporal satellite imagery. Considering the differences in different channels in the features of pre- and post-disaster images, we develop a dual-temporal aggregation module using paired features to excite change-sensitive channels of the features and learn the global change pattern. Since the nature of building damage caused by disasters is diverse in complex environments, we design a difference-attention module to exploit local correlations among the multi-level changes, which improves the ability to identify damage on different scales. Extensive experiments on the large-scale building damage assessment dataset xBD demonstrate that our approach provides new state-of-the-art results. Source code is publicly available at https://github.com/mj129/D2ANet.

识别地面上的动态变化，尤其是各种自然灾害引起的变化，对于评估损害的严重程度和指导灾难响应至关重要。然而，目前的灾害评估工作流程通常需要人工分析人员观察和识别受损建筑物，劳动强度大，不适合大规模灾区。在本文中，我们提出了一种差异感知注意网络 (D2ANet)，用于从双时相卫星图像同时进行建筑物定位和多级变化检测。考虑到灾前和灾后图像特征在不同通道中的差异，我们开发了一个双时态聚合模块，使用成对特征来激发特征的变化敏感通道并学习全局变化模式。由于灾害造成的建筑物损坏的性质在复杂环境中是多种多样的，我们设计了一个差异注意模块来利用多层次变化之间的局部相关性，从而提高了识别不同尺度损坏的能力。在大型建筑损坏评估数据集 xBD 上进行的大量实验表明，我们的方法提供了最新的最新结果。源代码可在 https://github.com/mj129/D2ANet 上公开获得。