  Building detection and change detection using remote sensing images can help
urban and rescue planning. Moreover, they can be used for building damage
assessment after natural disasters. Currently, most of the existing models for
building detection use only one image (pre-disaster image) to detect buildings.
This is based on the idea that post-disaster images reduce the model's
performance because of presence of destroyed buildings. In this paper, we
propose a siamese model, called SiamixFormer, which uses pre- and post-disaster
images as input. Our model has two encoders and has a hierarchical transformer
architecture. The output of each stage in both encoders is given to a temporal
transformer for feature fusion in a way that query is generated from
pre-disaster images and (key, value) is generated from post-disaster images. To
this end, temporal features are also considered in feature fusion. Another
advantage of using temporal transformers in feature fusion is that they can
better maintain large receptive fields generated by transformer encoders
compared with CNNs. Finally, the output of the temporal transformer is given to
a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD,
and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for
change detection and could outperform the state-of-the-art.
