Title
Social media geo-sensing services for EO missions under sensor web environment: Users sensing information about the Ya'an earthquake from Sina Weibo
Abstract
In this Web 2.0 era, billions of social media users contribute massive amounts of information that have already been presented in earth observation (EO) applications such as earthquake information reporting, flood monitoring, and wildfire alerting. Nevertheless, applying social media data in geo-sensing missions directly and effectively remains a challenge. In this paper, we examine how to design a social media geo-sensing service under a geospatial Sensor Web environment. First, we designed a social media information-retrieval (SMIR) framework that is capable of harvesting geo-referenced social media content and re-encoding these data with standard geospatial data format. Then, we studied how to apply a sensor planning service (SPS) to serve the SMIR for on-demand social media geo-sensing tasks. In the experimental section, we used Sina Weibo, a popular Chinese social networking site, to test the feasibility and capability of the proposed social media geo-sensing service for emergency EO tasks. We used the SPS to acquire earthquake reports and earthquake relief information from Weibo in connection with the 2013 Ya'an earthquake. The approach presented and architecture used might serve as central building blocks for a social media geo-sensing system that can help develop the information infrastructure for future EO missions.
Year
DOI
Venue
2017
10.1109/Agro-Geoinformatics.2017.8047032
2017 6th International Conference on Agro-Geoinformatics
Keywords
Field
DocType
Social media Information,Sensor Web,Earth Observation,Earthquake,Sina Weibo
Geospatial analysis,World Wide Web,Architecture,Social media,Social network,Computer science,Server,Earth observation,Sensor web,Information infrastructure
Conference
ISSN
ISBN
Citations 
2334-3168
978-1-5386-3885-9
0
PageRank 
References 
Authors
0.34
12
2
Name
Order
Citations
PageRank
Chao Yang18722.49
Wenwen Tian200.34