Analysis of Tourists’ Image of Seoul with Geotagged Photos using Convolutional Neural Networks

Abstract

In this study we aim to analyze the urban image of Seoul that tourists feel through the photos uploaded on Flickr, which is one of Social Network Service (SNS) platforms that people can share Geo-tagged photos. We first categorize the photos uploaded on the site by tourists and then performed the image mining by utilizing Convolutional Neural Network (CNN), which is one of the artificial neural networks with deep learning capability. In this study we are able to find out that tourists are interested in old palaces, historical monuments, stores, food, etc. in which are considered to be the signatured sightseeing elements in Seoul. Those key elements are differentiated from the major sightseeing attractions within Seoul. The purpose of this study is two folds: First, we analyze the image of Seoul by applying the technology of image mining with the photos uploaded on Flickr by tourists. Second, we draw some significant sightseeing factors by region of attraction where tourists prefer to visit within Seoul.

Extracting Representative Images of Tourist Attractions from Flickr by Combining an Improved Cluster Method and Multiple Deep Learning Models

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What makes a place special? Research on the locality of cities in the Yellow River and Rhine River Basins based on Street View Images

“…26 A convolutional neural network (CNN), one of the effective ways to realize machine learning, has been tested by many scholars. [27][28][29] Considering that the long process of urbanization in China has led to the difficulty in obtaining data, the research compared different urbanization development stages with a cross-regional scale. Germany, which has a relatively mature urbanization development, was a comparative case for China.…”