Semantics-enabled spatio-temporal modeling of earth observation data: An application to flood monitoring

Abstract

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 multitemporal 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 t1 have been demonstrated with promising results.