Issue No. 05 - Sept.-Oct. (2015 vol. 8)
Aibek Musaev , School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
De Wang , School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
Calton Pu , School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
Landslides are an illustrative example of multi-hazards, which can be caused by earthquakes, rainfalls and human activity among other reasons. Detection of landslides presents a significant challenge, since there are no physical sensors that would detect landslides directly. A more recent approach in detection of natural hazards, such as earthquakes, involves the use of social media. We propose a multi-service composition approach and describe LITMUS, which is a landslide detection service that combines data from both physical and social information services by filtering and then joining the information flow from those services based on their spatiotemporal features. Our results show that with such approach LITMUS detects 25 out of 27 landslides reported by USGS in December 2013 and 40 more landslide locations unreported by USGS during this period. LITMUS is a prototype tool that is used to investigate and implement research ideas in the area of disaster detection. We list some of the current work being done on refining the system that allows us to identify 137 landslide locations unreported by USGS during a more recent period of September 2014. Finally, we describe a live demonstration that displays landslide detection results on a web map in real-time.
Terrain factors, Sensors, Information services, Earthquakes, Twitter, Web services, Feeds
A. Musaev, D. Wang and C. Pu, "LITMUS: A Multi-Service Composition System for Landslide Detection," in IEEE Transactions on Services Computing, vol. 8, no. 5, pp. 715-726, 2015.