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

Conference · Fri Nov 01 04:00:00 EDT 2019

DOI:https://doi.org/10.1145/3356395.3365545· OSTI ID:1606861

Kurte, Kuldeep ^[1]; Potnis, Abhishek ^[2]; Durbha, Surya ^[2]

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1. ORNL
2. Indian Institute of Technology, Bombay

Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatiotemporal 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 spatiotemporal 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 spatiotemporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatiotemporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatiotemporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1606861
• Country of Publication: United States
• Language: English