Large-scale building damage assessment using a novel hierarchical transformer architecture on satellite images

Abstract

This paper presents damage assessment using a hierarchical transformer architecture (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 postdisaster 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 to the spatial features. The proposed network achieves state-of-the-art performance when tested on a large-scale disaster damage data set (xBD) for building localization and damage classification, as well as on LEVIR-CD data set for change detection tasks. In addition, this work introduces a new high-resolution satellite imagery data set, Ida-BD (related to 2021 Hurricane Ida in Louisiana) for domain adaptation. Further, it demonstrates an approach of using this data set by adapting the model with limited fine-tuning and hence applying the model to newly damaged areas with scarce data.