The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades. To tackle these challenges, we present a multi-task guided change detection network model, named as MTGCD-Net. The proposed model approaches the specific BCD problem by designing three auxiliary tasks, including: (1) a pixel-wise classification task to predict the roofs and facades of buildings; (2) an auxiliary task for learning the roof-to-footprint offsets of each building to account for the misalignment between building roof instances; and (3) an auxiliary task for learning the identical roof matching flow between bi-temporal aerial images to tackle the building roof mismatch problem. These auxiliary tasks provide indispensable and complementary building parsing and matching information. The predictions of the auxiliary tasks are finally fused to the main building change detection branch with a multi-modal distillation module. To train and test models for the BCD problem with off-nadir aerial images, we create a new benchmark dataset, named BANDON. Extensive experiments demonstrate that our model achieves superior performance over the previous state-of-the-art competitors.

翻译：离天底航空影像的倾斜视角特性给建筑变化检测（BCD）问题带来严峻挑战：相邻建筑的误匹配以及建筑立面的语义模糊性。为应对这些挑战，我们提出一种名为MTGCD-Net的多任务引导变化检测网络模型。该模型通过设计三项辅助任务来解决特定BCD问题，包括：（1）面向建筑屋顶与立面的像素级分类任务；（2）学习各建筑屋顶到基底偏移量的辅助任务，以处理建筑屋顶实例间的错位问题；（3）学习双时相航空影像间同源屋顶匹配流的辅助任务，以解决建筑屋顶误匹配问题。这些辅助任务提供不可或缺且互补的建筑解析与匹配信息。最终通过多模态蒸馏模块将辅助任务的预测结果融合至主建筑变化检测分支。为训练和评估离天底航空影像BCD问题的模型，我们创建了名为BANDON的新基准数据集。大量实验表明，我们的模型相比先前最先进的竞争者取得了更优性能。