  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.
