Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images
This addresses rapid emergency response for disaster management by improving damage classification, though it is incremental as it builds on existing transformer methods.
The paper tackles building damage assessment from satellite images after natural disasters by proposing a hierarchical transformer model, achieving state-of-the-art performance on the xBD and LEVIR-CD datasets and introducing a new dataset for domain adaptation.
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.