Learning Patterns of Tourist Movement and Photography from Geotagged
Photos at Archaeological Heritage Sites in Cuzco, Peru
- URL: http://arxiv.org/abs/2006.16424v1
- Date: Mon, 29 Jun 2020 22:49:59 GMT
- Title: Learning Patterns of Tourist Movement and Photography from Geotagged
Photos at Archaeological Heritage Sites in Cuzco, Peru
- Authors: Nicole D. Payntar, Wei-Lin Hsiao, R. Alan Covey, Kristen Grauman
- Abstract summary: We build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit in Cuzco, Peru.
Our goals are to (1) understand how the intensification of tourism intersects with heritage regulations and social media, aiding in the articulation of travel patterns across Cuzco's heritage landscape; and to (2) assess how aesthetic preferences and visuality become entangled with the rapidly evolving expectations of tourists, whose travel narratives are curated on social media and grounded in historic site representations.
- Score: 73.52315464582637
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The popularity of media sharing platforms in recent decades has provided an
abundance of open source data that remains underutilized by heritage scholars.
By pairing geotagged internet photographs with machine learning and computer
vision algorithms, we build upon the current theoretical discourse of
anthropology associated with visuality and heritage tourism to identify travel
patterns across a known archaeological heritage circuit, and quantify visual
culture and experiences in Cuzco, Peru. Leveraging large-scale in-the-wild
tourist photos, our goals are to (1) understand how the intensification of
tourism intersects with heritage regulations and social media, aiding in the
articulation of travel patterns across Cuzco's heritage landscape; and to (2)
assess how aesthetic preferences and visuality become entangled with the
rapidly evolving expectations of tourists, whose travel narratives are curated
on social media and grounded in historic site representations.
Related papers
- Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - (Re)framing Built Heritage through the Machinic Gaze [3.683202928838613]
We argue that the proliferation of machine learning and vision technologies create new scopic regimes for heritage.
We introduce the term machinic gaze' to conceptualise the reconfiguration of heritage representation via AI models.
arXiv Detail & Related papers (2023-10-06T23:48:01Z) - A domain adaptive deep learning solution for scanpath prediction of
paintings [66.46953851227454]
This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings.
We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans.
The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
arXiv Detail & Related papers (2022-09-22T22:27:08Z) - Geolocation of Cultural Heritage using Multi-View Knowledge Graph
Embedding [18.822364073669583]
We present a framework for ingesting knowledge about tangible cultural heritage entities.
We also propose a learning model for estimating the relative distance between a pair of cultural heritage entities.
arXiv Detail & Related papers (2022-09-08T08:32:34Z) - Detecting Damage Building Using Real-time Crowdsourced Images and
Transfer Learning [53.26496452886417]
This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter.
Using transfer learning and 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene.
The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey.
arXiv Detail & Related papers (2021-10-12T06:31:54Z) - Multi-Level Visual Similarity Based Personalized Tourist Attraction
Recommendation Using Geo-Tagged Photos [7.176673263585931]
We propose multi-level visual similarity based personalized tourist attraction recommendation using geo-tagged photos.
We define four visual similarity levels and introduce a corresponding quintuplet loss to embed the visual contents of photos.
To capture the significances of different photos, we exploit the self-attention mechanism to obtain the visual representations of users and tourist attractions.
arXiv Detail & Related papers (2021-09-17T01:34:15Z) - Machine Learning Advances aiding Recognition and Classification of
Indian Monuments and Landmarks [0.0]
Tourism in India plays a quintessential role in the country's economy with an estimated 9.2% GDP share for the year 2018.
The industry holds a huge potential for being the primary driver of the economy as observed in the nations of the Middle East like the United Arab Emirates.
Machine learning approaches revolving around the usage of monument pictures have been shown to be useful for rudimentary analysis of heritage sights.
arXiv Detail & Related papers (2021-07-29T15:01:02Z) - From Culture to Clothing: Discovering the World Events Behind A Century
of Fashion Images [100.20851232528925]
We propose a data-driven approach to identify specific cultural factors affecting the clothes people wear.
Our work is a first step towards a computational, scalable, and easily refreshable approach to link culture to clothing.
arXiv Detail & Related papers (2021-02-02T18:58:21Z) - IMAGO: A family photo album dataset for a socio-historical analysis of
the twentieth century [4.54108183549264]
We analyze the IMAGO dataset including photos belonging to family albums assembled at the University of Bologna's Rimini campus since 2004.
Following a deep learning-based approach, the IMAGO dataset has offered the opportunity of experimenting with photos taken between year 1845 and year 2009.
arXiv Detail & Related papers (2020-12-03T14:28:58Z) - Placepedia: Comprehensive Place Understanding with Multi-Faceted
Annotations [79.80036503792985]
We contribute Placepedia, a large-scale place dataset with more than 35M photos from 240K unique places.
Besides the photos, each place also comes with massive multi-faceted information, e.g. GDP, population, etc.
This dataset, with its large amount of data and rich annotations, allows various studies to be conducted.
arXiv Detail & Related papers (2020-07-07T20:17:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.