Tracking Areal Object Identity in Snapshot Sequences

Geosensor networks are deployed to detect and track dynamic geographic phenomena, or objects, over space and time. Object identity is a unique characteristic to distinguish different spatial objects. Based on tracking and analysis of the relations of object identities for example different classes of events can be derived. This paper introduces a spatiotemporal data model for the storage and maintenance of areal object lifespan in a decentralized network. Different from previous work, our algorithm uses snapshot sequences to analyze the relations of spatial objects, and it allows both abrupt and incremental changes. In contrast to previous approaches the presented approach can deal with change beyond incremental change. Our model can also become an essential component for analyzing static topological relations of spatial objects in a decentralized manner, or for detecting dynamic topological changes.