Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images

TL;DR

This paper introduces ahitra, a hierarchical transformer model for large-scale building damage assessment from satellite images, achieving state-of-the-art results and enabling effective domain adaptation with limited data.

Contribution

A novel hierarchical transformer architecture for damage assessment that leverages multi-resolution spatial features and demonstrates effective domain adaptation with a new dataset.

Findings

Achieved state-of-the-art performance on xBD and LEVIR-CD datasets.

Introduced the Ida-BD dataset for domain adaptation.

Showed effective damage assessment with limited fine-tuning.

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.