Buildings are primary locations for human activities and key focuses in the military domain. Rapidly detecting damaged/changed buildings (DCB) and conducting detailed assessments can effectively aid urbanization monitoring, disaster response, and humanitarian assistance. Currently, the tasks of object detection (OD) and change detection (CD) for DCB are almost independent of each other, making it difficult to simultaneously determine the location and details of changes. Based on this, we have designed a cross-task network called SDCINet, which integrates OD and CD, and have created four dual-task datasets focused on disasters and urbanization. SDCINet is a novel deep learning dual-task framework composed of a consistency encoder, differentiation decoder, and cross-task global attention collaboration module (CGAC). It is capable of modeling differential feature relationships based on bi-temporal images, performing end-to-end pixel-level prediction, and object bounding box regression. The bi-direction traction function of CGAC is used to deeply couple OD and CD tasks. Additionally, we collected bi-temporal images from 10 locations worldwide that experienced earthquakes, explosions, wars, and conflicts to construct two datasets specifically for damaged building OD and CD. We also constructed two datasets for changed building OD and CD based on two publicly available CD datasets. These four datasets can serve as data benchmarks for dual-task research on DCB. Using these datasets, we conducted extensive performance evaluations of 18 state-of-the-art models from the perspectives of OD, CD, and instance segmentation. Benchmark experimental results demonstrated the superior performance of SDCINet. Ablation experiments and evaluative analyses confirmed the effectiveness and unique value of CGAC.

中文翻译：

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SDCINet：一种新颖的跨任务集成网络，用于利用光学遥感图像分割和检测损坏/变化的建筑目标


建筑物是人类活动的主要场所，也是军事领域的重点。快速检测受损/改变的建筑物（DCB）并进行详细评估可以有效帮助城市化监测、灾害应对和人道主义援助。目前，DCB的对象检测（OD）和变化检测（CD）任务几乎是相互独立的，因此很难同时确定变化的位置和细节。基于此，我们设计了一个名为 SDCINet 的跨任务网络，它集成了 OD 和 CD，并创建了四个专注于灾害和城市化的双任务数据集。 SDCINet 是一种新颖的深度学习双任务框架，由一致性编码器、差分解码器和跨任务全局注意协作模块（CGAC）组成。它能够基于双时态图像对差异特征关系进行建模，执行端到端像素级预测和对象边界框回归。 CGAC的双向牵引功能用于深度耦合OD和CD任务。此外，我们还收集了全球 10 个经历过地震、爆炸、战争和冲突地点的双时态图像，构建了两个专门针对受损建筑 OD 和 CD 的数据集。我们还基于两个公开可用的 CD 数据集构建了两个用于更改建筑 OD 和 CD 的数据集。这四个数据集可以作为DCB双任务研究的数据基准。使用这些数据集，我们从 OD、CD 和实例分割的角度对 18 个最先进的模型进行了广泛的性能评估。基准实验结果证明了SDCINet的优越性能。 消融实验和评估分析证实了 CGAC 的有效性和独特价值。