This paper provides the vision of a unified spatial crowdsourcing platform that is designed to efficiently tackle different types of spatial tasks which have been gaining a lot of popularity in recent years. Several examples of spatial tasks are ride-sharing services, delivery services, translation tasks, and crowd-sensing tasks. While existing crowdsourcing platforms, such as Amazon Mechanical Turk and Upwork, are widely used to solve lots of general tasks, e.g., image labeling; using these marketplaces to solve spatial tasks results in low quality results. This paper identifies a set of characteristics for a unified spatial crowdsourcing environment and provides the core components of the platform that are required to empower the capability in solving different types of spatial tasks.
|Original language||English (US)|
|Title of host publication||Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings|
|Editors||Wei-Shinn Ku, Agnes Voisard, Haiquan Chen, Chang-Tien Lu, Siva Ravada, Matthias Renz, Yan Huang, Michael Gertz, Liang Tang, Chengyang Zhang, Erik Hoel, Xiaofang Zhou|
|Number of pages||5|
|State||Published - 2017|
|Event||15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States|
Duration: Aug 21 2017 → Aug 23 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||15th International Symposium on Spatial and Temporal Databases, SSTD 2017|
|Period||8/21/17 → 8/23/17|
Bibliographical noteFunding Information:
This work is partially supported by the National Science Foundation, USA, under Grants IIS-1525953, CNS-1512877, IIS-1218168, and IIS-0952977. 1 https://www.mturk.com/. 2 https://www.upwork.com/.
© Springer International Publishing AG 2017.