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As social network service usage is rapidly surging in our daily life, social network service data plays a crucial role in identifying region of attractions and analyzing tourism destination image. In recent years, the computer vision technology is just beginning to be applied in the tourism field through the transfer learning of a deep learning model. However, the pre-trained models have limitations of properly classifying the photos with the unique landscape or specific elements of the tourism destination. With the purpose of going beyond these limitations, we generated a tourists' photo classification reflecting regional characteristics and developed a deep learning model to classify photos according to this classification. Through the analysis of 168,216 Flickr photos, we analyzed the tourism destination image of Seoul. Key findings are that (1) tourists prefer to enjoy local food, to visit authentic traditional palaces, and to see inherent cityscape which can be uniquely enjoyed in Seoul, (2) tourist attractive factors differ by region of attractions, (3) tourist preferences differ by continent. This study has novelty in that it develops a tourist's photo classification suitable for regional characteristics and analyzes tourism destination image by classifying photos using an artificial intelligence technology. [ABSTRACT FROM AUTHOR]

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists' urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist' photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists' urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists' photos. [ABSTRACT FROM AUTHOR]