{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dahitra-damage-assessment-using-a-novel","title":"Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images","arxiv_id":"2208.02205","date":"2022-08-03","proceeding":null,"authors":["Navjot Kaur","Cheng-Chun Lee","Ali Mostafavi","Ali Mahdavi-Amiri"],"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.","url_abs":"https://arxiv.org/abs/2208.02205v3","url_pdf":"https://arxiv.org/pdf/2208.02205v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dahitra-damage-assessment-using-a-novel","repo_url":"https://github.com/nka77/dahitra","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"dahitra-damage-assessment-using-a-novel","repo_url":"https://github.com/YimingXiao98/DamageCAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"building-damage-assessment","task_name":"Building Damage Assessment"},{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}