This paper presents a method to extract traversed scenic routes from Volunteered Geographic Information (VGI) data sources. We used the footprints of images posted on Panoramio and Flickr, as well as websites where users uploaded tracks of traversed scenic routes. The state of California has been chosen as the test region. We discuss pros and cons of these VGI data sources for this task and describe the necessary steps to extract scenic routes. Measures for attributes of the route and the surrounding scenery can then be obtained for scenic routes and their corresponding fastest routes. This information will be utilized to identify relevant attributes that characterize scenicness. These attributes could in consequence be used as optimization criteria to search for scenic routes in trip planning applications.
{"title":"Extracting scenic routes from VGI data sources","authors":"M. Alivand, H. Hochmair","doi":"10.1145/2534732.2534743","DOIUrl":"https://doi.org/10.1145/2534732.2534743","url":null,"abstract":"This paper presents a method to extract traversed scenic routes from Volunteered Geographic Information (VGI) data sources. We used the footprints of images posted on Panoramio and Flickr, as well as websites where users uploaded tracks of traversed scenic routes. The state of California has been chosen as the test region. We discuss pros and cons of these VGI data sources for this task and describe the necessary steps to extract scenic routes. Measures for attributes of the route and the surrounding scenery can then be obtained for scenic routes and their corresponding fastest routes. This information will be utilized to identify relevant attributes that characterize scenicness. These attributes could in consequence be used as optimization criteria to search for scenic routes in trip planning applications.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117131685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
By utilizing large amount of crowd volunteered geo-tagged photos, existing research can successfully discover landmarks or attractive areas, mine travel patterns, find classical travel routes and recommend travel destinations or routes for inexperienced tourists. However, few of them focuses on a complicated real-life travel planning problem--planning multi-day and multi-stay (different places of accommodation) travel for tourist. By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.
{"title":"Multi-day and multi-stay travel planning using geo-tagged photos","authors":"Xun Li","doi":"10.1145/2534732.2534733","DOIUrl":"https://doi.org/10.1145/2534732.2534733","url":null,"abstract":"By utilizing large amount of crowd volunteered geo-tagged photos, existing research can successfully discover landmarks or attractive areas, mine travel patterns, find classical travel routes and recommend travel destinations or routes for inexperienced tourists. However, few of them focuses on a complicated real-life travel planning problem--planning multi-day and multi-stay (different places of accommodation) travel for tourist. By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115849161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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