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While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/crisisnlp.qcri.org\/medic\/index.html\">https:\/\/crisisnlp.qcri.org\/medic\/index.html<\/jats:ext-link>), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on <jats:italic>multi-task learning<\/jats:italic>, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/firojalam\/medic\">https:\/\/github.com\/firojalam\/medic<\/jats:ext-link>).<\/jats:p>","DOI":"10.1007\/s00521-022-07717-0","type":"journal-article","created":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T17:02:31Z","timestamp":1662224551000},"page":"2609-2632","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["MEDIC: a multi-task learning dataset for disaster image classification"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7172-1997","authenticated-orcid":false,"given":"Firoj","family":"Alam","sequence":"first","affiliation":[]},{"given":"Tanvirul","family":"Alam","sequence":"additional","affiliation":[]},{"given":"Md. Arid","family":"Hasan","sequence":"additional","affiliation":[]},{"given":"Abul","family":"Hasnat","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[]},{"given":"Ferda","family":"Ofli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"7717_CR1","unstructured":"Mouzannar H, Rizk Y, Awad M (2018) Damage identification in social media posts using multimodal deep learning. In: Proceedings of the international conference on information systems for crisis response and management. ISCRAM\u00a0\u201918. ISCRAM Association, pp 529\u2013543"},{"key":"7717_CR2","doi-asserted-by":"crossref","unstructured":"Nguyen DT, Ofli F, Imran M, Mitra P (2017) Damage assessment from social media imagery data during disasters. In: Proceedings of the 2017 IEEE\/acm international conference on advances in social networks analysis and mining. ASONAM\u00a0\u201917. 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