| Title: | Image-based analysis of tourist destination perceptions : a deep learning and spatial–temporal study in Slovenia |
|---|
| Authors: | ID Paliska, Dejan (Author)
ID Brezovec, Aleksandra (Author)
ID Sedmak, Gorazd (Author) |
|---|
| Files: |  RAZ_Paliska_Dejan_2026.pdf (2,93 MB)
MD5: 205F2B5FC512E26F00670871ACEA4CFF
 https://www.mdpi.com/2673-5768/7/2/52
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FTŠ Turistica - Turistica – College of Tourism Portorož
|
|---|
| 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 |
|---|
| Publication version: | Version of Record |
|---|
| Publication date: | 17.02.2026 |
|---|
| Year of publishing: | 2026 |
|---|
| Number of pages: | str. 1-20 |
|---|
| Numbering: | Vol. 7, iss. 2 |
|---|
| PID: | 20.500.12556/RUP-22666  |
|---|
| UDC: | 338.48 |
|---|
| ISSN on article: | 2673-5768 |
|---|
| DOI: | 10.3390/tourhosp7020052 |
|---|
| COBISS.SI-ID: | 268885251 |
|---|
| Publication date in RUP: | 18.02.2026 |
|---|
| Views: | 38 |
|---|
| Downloads: | 2 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
