DAHiTrA: Damage Assessment Using a Novel Hierarchical Transformer Architecture

• URL: http://arxiv.org/abs/2208.02205v1
• Date: Wed, 3 Aug 2022 16:41:39 GMT
• Title: DAHiTrA: Damage Assessment Using a Novel Hierarchical Transformer Architecture
• Authors: Navjot Kaur, Cheng-Chun Lee, Ali Mostafavi, Ali Mahdavi-Amiri
• Abstract summary: This paper presents DAHiTrA, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images. Satellite imagery provides real-time, high-coverage information. Deep-learning methods have shown to be promising in classifying building damage.
• Score: 4.162725423624233
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 hurricanes. An automated building damage assessment provides critical information for decision making and resource allocation for rapid emergency response. Satellite imagery provides real-time, high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment. In addition, deep-learning methods have shown to be promising in classifying building damage. 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 temporal difference 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, we introduce a new high-resolution satellite imagery dataset, Ida-BD (related to the 2021 Hurricane Ida in Louisiana in 2021, for domain adaptation to further evaluate the capability of the model to be applied to newly damaged areas with scarce data. The domain adaptation results indicate that the proposed model can be adapted to a new event with only limited fine-tuning. Hence, the proposed model advances the current state of the art through better performance and domain adaptation. Also, Ida-BD provides a higher-resolution annotated dataset for future studies in this field.

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