Automatic and periodic recompiling of building databases with up-to-date
high-resolution images has become a critical requirement for rapidly developing
urban environments. However, the architecture of most existing approaches for
change extraction attempts to learn features related to changes but ignores
objectives related to buildings. This inevitably leads to the generation of
significant pseudo-changes, due to factors such as seasonal changes in images
and the inclination of building fa\c{c}ades. To alleviate the above-mentioned
problems, we developed a contrastive learning approach by validating historical
building footprints against single up-to-date remotely sensed images. This
contrastive learning strategy allowed us to inject the semantics of buildings
into a pipeline for the detection of changes, which is achieved by increasing
the distinguishability of features of buildings from those of non-buildings. In
addition, to reduce the effects of inconsistencies between historical building
polygons and buildings in up-to-date images, we employed a deformable
convolutional neural network to learn offsets intuitively. In summary, we
formulated a multi-branch building extraction method that identifies newly
constructed and removed buildings, respectively. To validate our method, we
conducted comparative experiments using the public Wuhan University building
change detection dataset and a more practical dataset named SI-BU that we
established. Our method achieved F1 scores of 93.99% and 70.74% on the above
datasets, respectively. Moreover, when the data of the public dataset were
divided in the same manner as in previous related studies, our method achieved
an F1 score of 94.63%, which surpasses that of the state-of-the-art method.

本研究开发了对比学习方法，将历史建筑物的印记与单个最新的遥感图像进行验证，以注入建筑物的语义信息并增加其特征可辨度，避免因季节性变化和建筑立面倾斜等因素产生的伪变化，并利用可变形卷积神经网络减少历史建筑多边形与最新图像中建筑不一致的影响，识别新建和拆除的建筑物。在 Wuhan University 建筑变化检测数据集和自建的 SI-BU 数据集上，本方法分别获得了 93.99％和 70.74％的 F1 分数，并且在与该领域最先进的算法文章相同的数据集测试中，本方法表现出更好的性能。