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Image-based analysis of tourist destination perceptions : a deep learning and spatial–temporal study in Slovenia
Dejan Paliska, Aleksandra Brezovec, Gorazd Sedmak, 2026, original scientific article

Abstract: In the context of fierce competition among tourist destinations and increasing difficulty of differentiation, developing a strong destination image is particularly important. A comprehensive understanding of how tourists perceive destinations through user-generated images can help destination management organizations (DMOs) design more effective marketing strategies. This is especially relevant for destinations with spatially and temporally dispersed tourism resources and strong seasonal dynamics. This paper analyses inbound tourist photographs by combining deep learning techniques with spatial analysis to examine the spatial and temporal distribution of photo scenes and shifts in scene preferences among tourists. The study focuses on three distinct types of destinations in Slovenia—urban (Ljubljana), nature-based/alpine (Bled), and coastal (Piran, Izola, Koper)—providing insights into how image-based spatial scene analysis can inform destination marketing strategies. The results reveal significant spatial and temporal heterogeneity of scenes across micro destinations. Nature-based destinations exhibit lower topic entropy and fewer topic changes per user, whereas urban destinations show higher variability, with users changing topics on average five times per day. Seasonal effects are moderate: nature-based destinations display lower topic entropy in winter and higher in autumn and spring, coastal destinations show less pronounced seasonal variation, and urban destinations show almost none. These findings provide valuable insights into the spatial and temporal distribution of tourist interests and offer practical guidance for DMOs in strategic marketing planning.
Keywords: tourist destination image, user-generated content, deep learning, spatial-temporal analysis, destination marketing strategy
Published in RUP: 18.02.2026; Views: 959; Downloads: 14
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Erasmus+ mobility : empirical insights into Erasmus+ tourists' behaviour
Miha Lesjak, Emil Juvan, Eva Podovšovnik, 2020, original scientific article

Abstract: Erasmus+ students represent a large sub-segment of educational tourists, making this segment an attractive market for universities as well as destination marketing organisations. Unfortunately, very little is known about Erasmus+ students' travel behaviour; hence the present study aims at extending empirically supported knowledge about travel behaviour of students during their Erasmus+ mobility. Data was collected via an online survey among all Erasmus+ enrolling students in the academic year 2016/17 in Slovenia. The results show that 93% of the participants travelled during theirmobility. The level of studies aswell as gender affect students' travel behaviour, making the two characteristics immediately useful attributes when targeting Erasmus+ travellers. Based on perceived destination attributes, male students predominantly seek cities with attractive nightlife but female students look for easily accessible cities, which are safe and offer attractive cultural sites. These findings suggest that tourism providers, destination tourism organisations and universities should work hand in hand when designing personalised tourism experiences and their promotion among Erasmus+ students. This is crucial during the phase of planning Erasmus+ mobility, when students choose their destination and host university, as well as during students' Erasmus+ mobility, because Erasmus + students travel during their student mobility.
Keywords: Erasmus+ mobility, education, international students, destination attributes, tourist behaviour
Published in RUP: 30.11.2021; Views: 3625; Downloads: 63
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Competitiveness of Slovenia as a tourist destination
Doris Gomezelj Omerzel, 2006, original scientific article

Keywords: tourist destination, competitiveness, competitiveness indicators, tourism stakeholders, added value
Published in RUP: 15.10.2013; Views: 6033; Downloads: 83
Full text (155,37 KB)

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