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.
|Original language||English (US)|
|Title of host publication||26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018|
|Editors||Li Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel|
|Publisher||Association for Computing Machinery|
|Number of pages||11|
|State||Published - Nov 6 2018|
|Event||26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States|
Duration: Nov 6 2018 → Nov 9 2018
|Name||GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems|
|Other||26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018|
|Period||11/6/18 → 11/9/18|
Bibliographical noteFunding Information:
This work is partially supported by the National Science Foundation, USA, under Grants IIS-1525953 and CNS-1512877.
© 2018 Association for Computing Machinery.
- Geotagging Framework
- Spatial crowdsourcing