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

Abstract

AbstractThis 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.