  Accurate and fine-grained information about the extent of damage to buildings
is essential for humanitarian relief and disaster response. However, as the
most commonly used architecture in remote sensing interpretation tasks,
Convolutional Neural Networks (CNNs) have limited ability to model the
non-local relationship between pixels. Recently, Transformer architecture first
proposed for modeling long-range dependency in natural language processing has
shown promising results in computer vision tasks. Considering the frontier
advances of Transformer architecture in the computer vision field, in this
paper, we present the first attempt at designing a Transformer-based damage
assessment architecture (DamFormer). In DamFormer, a siamese Transformer
encoder is first constructed to extract non-local and representative deep
features from input multitemporal image-pairs. Then, a multitemporal fusion
module is designed to fuse information for downstream tasks. Finally, a
lightweight dual-tasks decoder aggregates multi-level features for final
prediction. To the best of our knowledge, it is the first time that such a deep
Transformer-based network is proposed for multitemporal remote sensing
interpretation tasks. The experimental results on the large-scale damage
assessment dataset xBD demonstrate the potential of the Transformer-based
architecture.
