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Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2012/paper/1153> ?p ?o. }

• 1153 creator david-crandall.
• 1153 creator gretchen-lebuhn.
• 1153 creator haipeng-zhang.
• 1153 creator mohammed-korayem.
• 1153 type InProceedings.
• 1153 label "Mining Photo-sharing Websites to Study Ecological Phenomena".
• 1153 sameAs 1153.
• 1153 abstract "The rapid rise in popularity of social sharing websites like Flickr and Twitter has created enormous collections of user-generated content online. Latent in these content collections are observations of the world: each photo is a visual snapshot of what the world looked like at a particular point in time and space, for example, while each tweet is a textual expression of the state of a person and their environment. Aggregating together these observations across millions of social sharing users could lead to new techniques for large-scale monitoring of the state of the world and how it is changing over time. In this paper we step towards that goal, showing that by analyzing the tags and image features of geo-tagged, time-stamped photos we can measure and quantify the occurrence of ecological phenomena including ground snow cover, snow fall and vegetation density. We compare several techniques for dealing with the large degree of noise in the dataset, and show how machine learning can be used to reduce errors caused by misleading tags and ambiguous visual content. We evaluate the accuracy of these techniques by comparing to ground truth data collected both by surface stations and by Earth-observing satellites. Besides the immediate application to ecology, our study gives insight into how to accurately crowd-source other types of information from large, noisy social sharing datasets.".
• 1153 hasAuthorList authorList.
• 1153 isPartOf proceedings.
• 1153 keyword "Data mining".
• 1153 keyword "ecology".
• 1153 keyword "photo collections".
• 1153 title "Mining Photo-sharing Websites to Study Ecological Phenomena".