Efficient method for POI/ROI discovery using flickr geotagged photos

• Verfasst von: Kuo, Chiao-Ling [VerfasserIn]
• Verfasst von: Zipf, Alexander [VerfasserIn]
• Titel: Efficient method for POI/ROI discovery using flickr geotagged photos
• Verf.angabe: by Chiao-Ling Kuo, Ta-Chien Chan, I.-Chun Fan, and Alexander Zipf
• E-Jahr: 2018
• Jahr: 16 March 2018
• Umfang: 19 S.
• Fussnoten: Gesehen am 13.11.2019
• Titel Quelle: Enthalten in: International Society for Photogrammetry and Remote SensingISPRS International Journal of Geo-Information
• Ort Quelle: Basel : MDPI, 2012
• Jahr Quelle: 2018
• Band/Heft Quelle: 7(2018,3) Artikel-Nummer 121, 19 Seiten
• ISSN Quelle: 2220-9964
• DOI: doi:10.3390/ijgi7030121
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Flickr geotagged photo
• Sach-SW: pattern discovery
• Sach-SW: point of interest (POI)
• Sach-SW: region of interest (ROI)
• Sach-SW: spatial overlap algorithm
• K10plus-PPN: 1681764970

Abstract

In the era of big data, ubiquitous Flickr geotagged photos have opened a considerable opportunity for discovering valuable geographic information. Point of interest (POI) and region of interest (ROI) are significant reference data that are widely used in geospatial applications. This study aims to develop an efficient method for POI/ROI discovery from Flickr. Attractive footprints in photos with a local maximum that is beneficial for distinguishing clusters are first exploited. Pattern discovery is combined with a novel algorithm, the spatial overlap (SO) algorithm, and the naming and merging method is conducted for attractive footprint clustering. POI and ROI, which are derived from the peak value and range of clusters, indicate the most popular location and range for appreciating attractions. The discovered ROIs have a particular spatial overlap available which means the satisfied region of ROIs can be shared for appreciating attractions. The developed method is demonstrated in two study areas in Taiwan: Tainan and Taipei, which are the oldest and densest cities, respectively. Results show that the discovered POI/ROIs nearly match the official data in Tainan, whereas more commercial POI/ROIs are discovered in Taipei by the algorithm than official data. Meanwhile, our method can address the clustering issue in a dense area.