Abstract
The wide adoption of digital technologies in the cultural heritage sector has promoted the emergence of new, distributed ways of working, communicating, and investigating cultural products and services. In particular, collaborative online platforms and crowdsourcing mechanisms have been widely adopted in the effort to solicit input from the community and promote engagement. In this work, we provide an extensive analysis of the Wiki Loves Monuments initiative, an annual, international photography contest in which volunteers are invited to take pictures of the built cultural heritage and upload them to Wikimedia Commons. We explore the geographical, temporal, and topical dimensions across the 2010–2021 editions. We first adopt a set of CNN-based artificial systems that allow the learning of deep scene features for various scene recognition tasks, exploring cross-country (dis)similarities. To overcome the rigidity of the framework based on scene descriptors, we train a deep convolutional neural network model to label a photo with its country of origin. The resulting model captures the best representation of a heritage site uploaded in a country, and it allows the domain experts to explore the complexity of cross-national architectural styles. Finally, as a validation step, we explore the link between architectural heritage and intangible cultural values, operationalized using the framework developed within the World Value Survey research program. We observe that cross-country cultural similarities match to a fair extent the interrelations emerging in the architectural domain. We think this study contributes to highlighting the richness and the potential of the Wikimedia data and tools ecosystem to act as a scientific object for art historians, iconologists, and archaeologists.
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