Towards a Unified Spatial Crowdsourcing Platform

Jonathan, Christopher; Mokbel, Mohamed F.

doi:10.1007/978-3-319-64367-0_20

Cited by 3 publications

(1 citation statement)

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“…Several works have been conducted in studying spatial crowdsourcing where each crowdsourced task contains a spatial information about the task and/or the workers. Spatial crowdsourcing frameworks [10,14,27,28] require workers to physically go to the location of the task, e.g., take a picture of an object. However, these applications use one main assumption that the task's location to be known in advance.…”

Section: Related Workmentioning

confidence: 99%

Stella

Jonathan

Mokbel

2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Geotagged data (e.g. images or news items) have empowered various important applications, e.g., search engines and news agencies. However, the lack of available geotagged data significantly reduces the impact of such applications. Meanwhile, existing geotagging approaches rely on the existence of prior knowledge, e.g., accurate training dataset for machine learning techniques. This paper presents Stella; a crowdsourcing framework for image geotagging. The high accuracy of Stella is resulted by being able to recruit workers near the image location even without knowing its location. In addition, Stella also return its confidence about the reported location to help users in understanding the result quality. Experimental evaluation shows that Stella consistently geotags an image with an average of 95% accuracy and 90% of confidence.

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Section: Related Workmentioning

confidence: 99%