A geospatial ontological model for remote sensing scene semantic knowledge mining for the flood disaster

Numerous remote sensing applications - flood monitoring, forest fires monitoring, earthquake analysis etc. require users to query satellite images based on their content. Such requirements have led to the evolution of Content-based Image Information Mining Systems over the last decade. Recent developments in the area of Image Information Mining(IIM) are geared towards bridging the gap between low level image features and higher-level semantics. This research focuses on improving the semantic understanding of a remote sensing scene during the flood disaster from a spatio-contextual standpoint. During a flood occurrence, it is crucial to understand the flood inundation and receding patterns in context to the spatial configurations of the land-use/land-cover in the flooded regions. This study focuses on bridging the spatio-contextual semantic gap in understanding of the remote sensing imagery during a flood, thereby attempting to improve the machine interpretability of a flood remote sensing imagery. In this regard, the Flood Scene Ontology (FSO) has been developed to mine the topological and directional knowledge in context to the flood disaster phenomenon. The FSO is envisaged to form the basis for developing applications that would utilize the spatio-contextual semantics of the flood disaster to aid in the Disaster Assessment and Management process. This paper describes the conceptual framework that was developed to address the same.