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Publication

Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring...

by Kuldeep R Kurte, Abhishek Potnis, Surya Durbha
Publication Type
Conference Paper
Book Title
ARIC'19: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities
Publication Date
Page Numbers
41 to 50
Conference Name
ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities
Conference Location
Chicago, Illinois, United States of America
Conference Sponsor
ACM, Microsoft, Apple, HERE, Lyft, ORACLE, Google, Springer
Conference Date
-

Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatio-temporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatio-temporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatio-temporal information from a series of multi-temporal RS images that are acquired during a flood disaster event. We represent this spatio-temporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatio-temporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1have been demonstrated with promising results.