This document presents a study on using computer vision techniques to analyze visual destination branding on Instagram. Specifically, it discusses fine-tuning a state-of-the-art deep learning model to classify destination photographs into exclusive and exhaustive cognitive attributes of destination image. This trained model can then be used to extract and compare multi-dimensional vector representations of destinations' visual brand images based on the classification of photo datasets from their Instagram hashtags. The implications and opportunities identified from comparing destinations' visual brand images can help tourism marketers assess marketing success and identify brand attributes to promote or improve upon.

1. How do destinations relate to one
another? A study of visual
destination branding on Instagram
Lyndon Nixon
MODUL University Vienna
School of Applied Data Science
Vienna, Austria
lyndon.nixon@modul.ac.at

2. Visual Destination Branding
• Destination brand
• Related to e-tourism research on
destination image
• Visual destination brand (VDB)
• Consumer’s image of a destination
increasingly informed through
visual media
18 January 2023 ENTER2023 – Research Track Page 2

3. Rise of Computer Vision (CV)
• Earliest work on examining destination
photography relied on manual annotation
of small samples
• Neural Networks & Deep Learning =
advances in accuracy of automated image
classification
• Standard benchmark: AI models trained
on large generic image sets (ImageNet)
18 January 2023 ENTER2019 – Research Track Page 3

4. Use of CV in e-tourism
• E-tourism publications have used available
(pre-trained) models to classify photographs
with (generic, broad) labels, then cluster labels
to determine the primary categories relevant
to the VDB
• Limitations:
• Results can not be compared across papers
• Result depends on the prior training of the AI
model, e.g. how the training data set was
annotated
• Labels may not be complete, e.g. ImageNet does
not have ‘desert’
18 January 2023 ENTER2019 – Research Track Page 4
Nixon, L. (2018). “Assessing the usefulness of online image
annotation services for destination image measurement”, ENTER2018

5. Contribution of my work
• 1. Determine a set of labels for cognitive
attributes of destination image which are
exclusive and exhaustive
• 2. Fine-tune a state-of-the-art CV model
for those labels by training it on relevant
destination photography
• 3. Demonstrate accuracy by evaluating on
a ground truth data set
18 January 2023 ENTER2019 – Research Track Page 5

6. Measuring VDB with the model
• Our approach is to classify each photo with a
single label (class with the highest confidence, as
long as it is above a certain threshold)
• The classification of an image data set can be
represented as a multi-dimensional vector (each
feature is one of our visual classes)
• Bali’s vector is e.g. [0.07124011 0.04309587 0.10202287
0.01319261 0.02022867 0.03605981 0.06068602
0.06508355 0.09146878 0.01055409 0.01319261
0.01055409 0.09234828 0.05013193 0.08531223 0.0351803
0.02726473 0.17238347]
18 January 2023 ENTER2019 – Research Track Page 6
Bali #balitravel, #visitbali 1137
New Orleans #onetimeinnola 725
Marrakesh #visitmarrakech 934
Maldives #visitmaldives 1045
Paris #jetaimeparis 625
Dubai #visitdubai 813
Bora Bora #visit_borabora 1258
New York #itstimefornyc 745
Dubrovnik #lovedubrovnik 695

7. Comparison of VDBs
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8. Comparison of VDBs
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9. Implications
18 January 2023 ENTER2019 – Research Track Page 9
• The extracted VDB needs to be compared with the tourism
marketer’s intended VDB to assess marketing success
• Bali’s VDB for beaches is weaker than Bora Bora or Maldives – as a beach
destination, this could be a concern for destination marketing
• Its VDB for roads & traffic is higher – depends on whether Bali wants to be
seen as having more urban infrastructure than its beach competitors
• Weak or strong VDB features can be seen as gaps and opportunities
• Marrakesh is weak in historical buildings, this could be promoted more
• New Orleans is strong in shops & markets as well as gastronomy, this is an
opportunity to move away from branding as an entertainment destination

10. Conclusion
18 January 2023 ENTER2019 – Research Track Page 10
• The trained model weights are available on HuggingFace and can be
loaded into your neural networks for instant classification or
additional fine tuning
• https://huggingface.co/lyndonnixon/destination-image-classifier
• The ground truth data is also available for benchmarking new models
against mine (BEiT-L Transformer achieving 0.94 accuracy)
• https://bit.ly/visualdestination
• This creates the opportunity to compare CV models for e-tourism
THANK YOU!