Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
Abstract
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.
Keywords
Cite
@article{arxiv.2208.02205,
title = {Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images},
author = {Navjot Kaur and Cheng-Chun Lee and Ali Mostafavi and Ali Mahdavi-Amiri},
journal= {arXiv preprint arXiv:2208.02205},
year = {2023}
}