Detection of disaster-affected cultural heritage sites from social media images using deep learning techniques

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• dc.contributor.author Kumar, Pakhee
• dc.contributor.author Ofli, Ferda
• dc.contributor.author Imran, Muhammad
• dc.contributor.author Castillo, Carlos
• dc.date.accessioned 2021-02-05T07:17:07Z
• dc.date.available 2021-02-05T07:17:07Z
• dc.date.issued 2020
• dc.description.abstract This article describes a method for early detection of disaster-related damage to cultural heritage. It is based on data from social media, a timely and large-scale data source that is nevertheless quite noisy. First, we collect images posted on social media that may refer to a cultural heritage site. Then, we automatically categorize these images according to two dimensions: whether they are indeed a photo in which a cultural heritage resource is the main subject, and whether they represent damage. Both categorizations are challenging image classification tasks, given the ambiguity of these visual categories; we tackle both tasks using a convolutional neural network. We test our methodology on a large collection of thousands of images from the web and social media, which exhibit the diversity and noise that is typical of these sources, and contain buildings and other architectural elements, heritage and not-heritage, damaged by disasters as well as intact. Our results show that while the automatic classification is not perfect, it can greatly reduce the manual effort required to find photos of damaged cultural heritage by accurately detecting relevant candidates to be examined by a cultural heritage professional.en
• dc.description.sponsorship C. Castillo is partially funded by La Caixa project LCF/PR/PR16/11110009.
• dc.format.mimetype application/pdf
• dc.identifier.citation Kumar P, Ofli F, Imran M, Castillo C. Detection of disaster-affected cultural heritage sites from social media images using deep learning techniques. J Comput Cult Herit. 2020 Aug;13(3):23. DOI: 10.1145/3383314
• dc.identifier.doi http://dx.doi.org/10.1145/3383314
• dc.identifier.issn 1556-4673
• dc.identifier.uri http://hdl.handle.net/10230/46354
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof Journal on Computing and Cultural Heritage. 2020 Aug;13(3):23
• dc.rights © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in J Comput Cult Herit, 2020 Aug;13(3):23 http://doi.acm.org/10.1145/3383314
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Cultural heritage sitesen
• dc.subject.keyword Social mediaen
• dc.subject.keyword Damage assessmenten
• dc.subject.keyword Deep learningen
• dc.title Detection of disaster-affected cultural heritage sites from social media images using deep learning techniquesen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion