ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 95–102, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-95-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 95–102, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-95-2020

  03 Aug 2020

03 Aug 2020

ENRICHING WALKING ROUTES WITH TOURISM ATTRACTIONS RETRIEVED FROM CROWDSOURCED USER GENERATED DATA

M. Mor and S. Dalyot M. Mor and S. Dalyot
  • Mapping and Geoinformation Engineering, The Technion, Haifa, Israel

Keywords: Social Media, Trajectory Reconstruction, Crowdsource Geotagged Photos, Tourism Context

Abstract. It is always a tourism challenge – and aspiration – to discover scenery routes and tourism attractions in unfamiliar areas. Tourism information is getting more extensive, comprehensive and complex, so first-time tourists have to manage and mine large volumes of data to better plan their trip. Nowadays, geotagged photos are uploaded by users to social media photo-sharing online websites, which become more popular and commonly used by travelers to share their tourism experiences. Handling, mining and interpreting these user-generated ‘digital footprints’ can be used to reconstruct travel trajectories of users to recover their activity and knowledge. In this research, we showcase Flickr geotagged crowdsource photo database as a source for mining users’ trajectories to effectively compute walking tourism routes. Our methodology mines tourism context by conceptualizing a set of adaptive spatiotemporal descriptors to identify photographers that show tourism activity of first-time visitors. By implementing spatial clustering, we find popular locations that are traversed by these tourism-oriented photographers’ trajectories. To analyze our approach, we develop a greedy route computation algorithm that seeks the most popular traversed locations between origin and destination points defined by the user. Results for two cities are presented, proving the robust mining and retrieving of valuable tourism context and information from social media photos. We evaluate and validate our results by comparing the computed walking routes to recognized tourism information. The computed walking routes are scenery and pass through the main popular tourism sights and landmarks in the city, including additional attractive places that are frequently visited by tourism-photographers.