This paper 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 non-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.
The purpose of this paper was to understand how Twitter users responded to the cultural heritage damaged during the 2015 Nepal earthquake. This paper utilizes 201,457 tweets (including retweets) from three different data sets. The analysis shows that approximately 4% of tweets were regarding cultural heritage. Moreover, asymmetrical information was available on Twitter regarding cultural heritage during the Nepal earthquake, that is not every site received equal attention from the public. Damaged sites received more attention than unaffected sites. The content of tweets can be divided into five categories: information, sentiment, memory, action and noise. Most people (89.1%) used Twitter during the disaster to disseminate information regarding damaged cultural heritage sites.
Illustrating the application of crowdsourcing in disaster response before the Internet age, this paper addresses two key questions: How did the people respond to the cultural heritage damaged during the 1966 Florence Flood? How were they motivated to do so? Content analysis of 180 out of 753 correspondence items from the archives of Fondazione Centro Studi Sull'Arte Licia e Carlo Ludovico Ragghianti in Lucca, Italy shows that the committee received contributions in the form of money, materials, volunteers and knowledge from di↵erent parts of the world. The most popular of all contributions, however, was money.Four main factors were found to be motivating people to contribute: 1) the call to participate, 2) the media, 3) influencers, and 4) memory of the city. Of key importance, this paper emphasizes: how to initiate a crowdsourcing campaign to restore cultural heritage, who will contribute or is most likely to contribute and how to motivate people to contribute.
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