  This paper presents \dahitra, a novel deep-learning model with hierarchical
transformers to classify building damages based on satellite images in the
aftermath of natural disasters. Satellite imagery provides real-time and
high-coverage information and offers opportunities to inform large-scale
post-disaster building damage assessment, which is critical for rapid emergency
response. In this work, a novel transformer-based network is proposed for
assessing building damage. This network leverages hierarchical spatial features
of multiple resolutions and captures the temporal differences in the feature
domain after applying a transformer encoder on the spatial features. The
proposed network achieves state-of-the-art performance when tested on a
large-scale disaster damage dataset (xBD) for building localization and damage
classification, as well as on LEVIR-CD dataset for change detection tasks. In
addition, this work introduces a new high-resolution satellite imagery dataset,
Ida-BD (related to 2021 Hurricane Ida in Louisiana in 2021) for domain
adaptation. Further, it demonstrates an approach of using this dataset by
adapting the model with limited fine-tuning and hence applying the model to
newly damaged areas with scarce data.
