Spatial content-based scene similarity assessment

Scene comparison and matching is a fundamental operation in geoinformatics. However, existing solutions are rather inadequate to support scene similarity assessment when comparing datasets collected from diverse sources especially ones that are available in diverse modalities (e.g. comparing image to vector datasets), or represent different time instances and thus differ partially in their content. In this paper we introduce a two-stage scene similarity assessment and matching framework that makes use of spatial scene content to compare and match two scenes as they may be captured in two different datasets (e.g. an aerial image and a map). At first stage our approach makes use of a matching algorithm based on the comparison of attributed graphs, where linear feature networks (e.g. road networks) are transformed into graphs and network properties are expressed through graph-embedded invariant attributes. By matching these graphs we can assess the similarity between two scenes. At the second stage, we proceed with an invariant scene comparison metric that incorporates additional scene content in the form of object configurations present within individual road network loops (e.g. building arrangements within city squares). By combining diverse but co-located pieces of information (e.g. roads and buildings) in an integrated process, our algorithm supports scene comparison and matching even when comparing heterogeneous datasets. In this paper we present key theoretical concepts and provide experimental results to demonstrate the performance of the proposed approach.