Developments in AI such as neural networks, deep learning and AGI have meant that computational understanding of images and videos appears easier than ever. However for tourism and destination marketing it is important to consider how to fine tune models to meet the needs of touristic understanding of user photography.

1. New Opportunities for Understanding
Tourism Photography through AI
Dr Lyndon Nixon
School of Applied Data Science
MODUL University Vienna
lyndon.nixon@modul.ac.at

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© 2011 MODUL University Vienna
Why care about visual destination brand?

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© 2011 MODUL University Vienna
e.g. visual destination brand on Instagram
• Largest visual social network with 1.2
billion active users (1)
○ 62.8% of Instagrammers are aged
18-34
• Every week more than 1 million travel
related hashtags are searched (2)
• 48% of users use the visual content for
travel decision making and 35% use it to
discover new places (3)
(1) https://datareportal.com/reports/digital-2021-global-overview-report
(2) https://www.wexinc.com/insights/resources/u-s-travel-trends-2019-report/
(3) https://www.mediapost.com/publications/article/263167/instagram-the-place-to-be-for-
travel-brands.html

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© 2011 MODUL University Vienna
The emergence of Computer Vision
• Researchers had to manually
annotate small samples of
visual media
• Neural Networks + Deep
Learning = significant
advances in computer vision
• Current benchmark: AI models
trained and tested on a large
generic image dataset
(ImageNet)
https://paperswithcode.com/sota/image-classification-on-imagenet

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© 2011 MODUL University Vienna
Fine tuning a CV model for VDB
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

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© 2011 MODUL University Vienna
Fine tuning a CV model for VDB

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© 2011 MODUL University Vienna
Fine tuning a CV model for VDB

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© 2011 MODUL University Vienna
Measuring VDB with the classifier
▪ Our approach is to classify each photo
with a single label (class with the
highest confidence above a certain
threshold)
▪ The classification of an image data set
can be represented as a multi-
dimensional vector
▪ 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]
Try it at
https://huggingface.co/
lyndonnixon/destination
-image-classifier

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© 2011 MODUL University Vienna
Study for DMO marketing
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

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© 2011 MODUL University Vienna
Study for DMO marketing

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© 2011 MODUL University Vienna
How this leads to smarter tourism
▪ DMOs and tourism stakeholders today lack vital information
on how their destinations (or tourism offers) are being
presented online through user generated image and video
▪ Visual classifiers offer a means to measure visual destination
brand accurately and at scale
BUT
▪ What is the right set of visual categories to use in quantifying
visual destination brand?
▪ How to deal with cultural and regional differences, both
across destinations and in terms of the viewer?
▪ We still miss studies on how (actual or intended) destination
brands relate (if at all ) to tourism metrics like number of
visitors, overnight stays, bookings, Website visits, etc.

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© 2011 MODUL University Vienna
Tourism can only get smarter…
Image understanding
tech is more
accessible than ever.
ChatGPT 4 and other
tools will describe
images to you. Is this
the next step after
the visual classifier?
Screenshot from demo of BLIP: Bootstrapping
Language-Image Pre-training for Unified Vision-
Language Understanding and Generation at
https://huggingface.co/Salesforce/blip-image-
captioning-base

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© 2011 MODUL University Vienna
THANK YOU for your attention
Dr Lyndon Nixon
lyndon.nixon@modul.ac.at