Stella: Geotagging images via crowdsourcing

Christopher Jonathan, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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 languageEnglish
Title of host publication26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
EditorsLi Xiong, Roberto Tamassia, Kashani Farnoush Banaei, Ralf Hartmut Guting, Erik Hoel
PublisherAssociation for Computing Machinery
Pages168-178
Number of pages11
ISBN (Electronic)9781450358897
DOIs
Publication statusPublished - 6 Nov 2018
Event26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 - Seattle, United States
Duration: 6 Nov 20189 Nov 2018

Other

Other26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
CountryUnited States
CitySeattle
Period6/11/189/11/18

Fingerprint

Confidence
Search engines
Learning systems
Search Engine
Prior Knowledge
Experimental Evaluation
engine
Machine Learning
High Accuracy
Training
Framework
evaluation
machine learning

Keywords

  • Crowdsourcing
  • Geotagging Framework
  • Spatial crowdsourcing

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Jonathan, C., & Mokbel, M. (2018). Stella: Geotagging images via crowdsourcing. In L. Xiong, R. Tamassia, K. F. Banaei, R. H. Guting, & E. Hoel (Eds.), 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018 (pp. 168-178). Association for Computing Machinery. https://doi.org/10.1145/3274895.3274902

Stella : Geotagging images via crowdsourcing. / Jonathan, Christopher; Mokbel, Mohamed.

26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018. ed. / Li Xiong; Roberto Tamassia; Kashani Farnoush Banaei; Ralf Hartmut Guting; Erik Hoel. Association for Computing Machinery, 2018. p. 168-178.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jonathan, C & Mokbel, M 2018, Stella: Geotagging images via crowdsourcing. in L Xiong, R Tamassia, KF Banaei, RH Guting & E Hoel (eds), 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018. Association for Computing Machinery, pp. 168-178, 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018, Seattle, United States, 6/11/18. https://doi.org/10.1145/3274895.3274902
Jonathan C, Mokbel M. Stella: Geotagging images via crowdsourcing. In Xiong L, Tamassia R, Banaei KF, Guting RH, Hoel E, editors, 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018. Association for Computing Machinery. 2018. p. 168-178 https://doi.org/10.1145/3274895.3274902
Jonathan, Christopher ; Mokbel, Mohamed. / Stella : Geotagging images via crowdsourcing. 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018. editor / Li Xiong ; Roberto Tamassia ; Kashani Farnoush Banaei ; Ralf Hartmut Guting ; Erik Hoel. Association for Computing Machinery, 2018. pp. 168-178
@inproceedings{840380738ec346899a1af786b9f9d7ed,
title = "Stella: Geotagging images via crowdsourcing",
abstract = "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.",
keywords = "Crowdsourcing, Geotagging Framework, Spatial crowdsourcing",
author = "Christopher Jonathan and Mohamed Mokbel",
year = "2018",
month = "11",
day = "6",
doi = "10.1145/3274895.3274902",
language = "English",
pages = "168--178",
editor = "Li Xiong and Roberto Tamassia and Banaei, {Kashani Farnoush} and Guting, {Ralf Hartmut} and Erik Hoel",
booktitle = "26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - Stella

T2 - Geotagging images via crowdsourcing

AU - Jonathan, Christopher

AU - Mokbel, Mohamed

PY - 2018/11/6

Y1 - 2018/11/6

N2 - 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.

AB - 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.

KW - Crowdsourcing

KW - Geotagging Framework

KW - Spatial crowdsourcing

UR - http://www.scopus.com/inward/record.url?scp=85058623807&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058623807&partnerID=8YFLogxK

U2 - 10.1145/3274895.3274902

DO - 10.1145/3274895.3274902

M3 - Conference contribution

AN - SCOPUS:85058623807

SP - 168

EP - 178

BT - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018

A2 - Xiong, Li

A2 - Tamassia, Roberto

A2 - Banaei, Kashani Farnoush

A2 - Guting, Ralf Hartmut

A2 - Hoel, Erik

PB - Association for Computing Machinery

ER -