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A streamlined model is crucial for assessing the impact of unexpected disasters. Previous methods rely on training with historical disaster data to evaluate the damage caused. However, in practice, this approach rarely achieves acceptable performance due to domain shift. Therefore, existing models need to be adapted to the emergent disaster quickly. A promising way to achieve this goal is through unsupervised domain adaptation (UDA). To this end, many advancements have been made toward better transferability of the model, including the Domain Adversarial Neural Network (DANN), which utilizes adversarial training to attain a domain-invariant feature extractor. The Adversarial sliced Wasserstein Domain Adaptation Network (AWDAN) further improves on DANN by using sliced Wasserstein distance as a measure between the features extracted from the source and target domains. Inspired by these advancements, we explore the utility of UDA approaches in the disaster response domain. Specifically, we perform extensive experiments on real-world images collected from Twitter during four major disasters. We train a total of 216 models to benchmark six methods across all possible source\u2013target domain combinations. We improve the performance of the previous state-of-the-art DANN-based method for rapid damage assessment by enhancing it with a deeper backbone architecture to learn better feature representations. Furthermore, we adopt AWDAN to more effectively mitigate the distribution shift in data obtained from different disaster events. Experimental results demonstrate that the proposed approach achieves statistically significant performance gains, with up to 11.4% improvement in F1-score and 8.9% improvement in accuracy over the source-only model, consistently outperforming several state-of-the-art domain adaptation frameworks, including DANN, CORAL, MMD, and CDAN.<\/jats:p>","DOI":"10.1007\/s00521-026-12151-7","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:32:54Z","timestamp":1780554774000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid disaster damage assessment using deep adversarial sliced Wasserstein domain adaptation"],"prefix":"10.1007","volume":"38","author":[{"given":"Fatma","family":"AlNaimi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz","family":"Al-Homaid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferda","family":"Ofli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-5519","authenticated-orcid":false,"given":"Abdelkader","family":"Baggag","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"issue":"4","key":"12151_CR1","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1109\/TNNLS.2020.2988928","volume":"32","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Zhuang F, Wang J, Ke G, Chen J, Bian J, Xiong H, He Q (2020) Deep subdomain adaptation network for image classification. 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