Photo2Trip: generating travel routes from geo-tagged photos for trip planning

By Xin Lu, Changhu Wang, Jiang-Ming Yang, Yanwei Pang, Lei Zhang. ACM Multimedia, 2010

312 citations recorded by the source corpus.

Travel route planning is an important step for a tourist to prepare his/her trip. As a common scenario, a tourist usually asks the following questions when he/she is planning his/her trip in an unfamiliar place: 1) Are there any travel route suggestions for a one-day or three-day trip in Beijing? 2) What is the most popular travel path within the Forbidden City? To facilitate a tourist's trip planning, in this paper, we target at solving the problem of automatic travel route planning. We propose to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences. As the footprints of tourists at memorable destinations, the geo-tagged photos could be naturally used to discover the travel paths within a destination (attractions/landmarks) and travel routes between destinations. Based on the information discovered from geo-tagged photos, we can provide a customized trip plan for a tourist, i.e., the popular destinations to visit, the visiting order of destinations, the time arrangement in each destination, and the typical travel path within each destination. Users are also enabled to specify personal preference such as visiting location, visiting time/season, travel duration, and destination style in an interactive manner to guide the system. Owning to 20 million geo-tagged photos and 200,000 travelogues, an online system has been developed to help users plan travel routes for over 30,000 attractions/landmarks in more than 100 countries and territories. Experimental results show the intelligence and effectiveness of the proposed framework.

Subfields: Computer Vision and Pattern Recognition, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c8e1c1ac60d391c47dfdecd0aa10e757aa396aea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jie Bao
2. Gao Cong
3. J. Shane Culpepper
4. David Wilkie
5. Yue Shi
6. Martha A. Larson
7. Alan Hanjalic
8. Mohamed F. Mokbel

In Transportation

1. Yu Zheng
2. Jie Bao
3. David Wilkie
4. Mohamed F. Mokbel
5. Gao Cong
6. Guoliang Li
7. Haitao Yuan
8. Zhifeng Bao

In Signal Processing

1. Yu Zheng
2. Jie Bao
3. Guoliang Li
4. Haitao Yuan
5. David Wilkie
6. Mohamed F. Mokbel
7. Gao Cong
8. Zhifeng Bao

Citations in the corpus

• A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “[115] leveraged existing travel clues recovered from 20 million geo-tagged photos to suggest customized travel route plans according to user preferences.”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “For example, Lu et al. [215] first leverage the geo-tagged photographs to recover travel clues and then recommend routes based on users’ preference.”
• Collaborative Filtering beyond the User-Item Matrix (2014, background): “In a photo sharing site, geotags of a photo may indicate that the uploader of the photo has been to that location (or nearby) [Arase et al. 2010; Kurashima et al. 2010; Lu et al. 2010].”
• Recommendations in location-based social networks: a survey (2015, background): “A number of sequential location recommender systems have been proposed based on either users’ geo-tagged social media posts [58, 63, 65, 94, 102] and users’ GPS trajectories [15, 28, 35, 36, 121, 122, 133].”