Spatiotemporal Analysis with St Helixes

Efficient modelling of spatiotemporal change as it is depicted in multitemporal imagery is an important step towards the efficient analysis and management of large motion imagery (MI) datasets. Furthermore, the development of concise representation schemes of MI content is essential for the search, retrieval, interchange, query, and visualization of the information included in MI datasets. Towards this goal this paper deals with the concise modelling of spatiotemporal change as it is captured in collections of MI data, and the development of spatiotemporal similarity metrics to compare the evolution of different objects. Helixes represent both movement and deformation in a single concise model, and are therefore highly suitable to communicate the evolution of phenomena as they are captured e.g. in sequences of imagery. This integration of movement and deformation information in a single model is an extension of existing solutions, and is highly suitable for the summarization of motion imagery datasets, especially within the context of geospatial applications. In this paper we present the spatiotemporal helix model, its use to support spatiotemporal queries, and spatiotemporal similarity metrics for the comparison of helixes. These metrics allow us to compare the behavior of different objects over time, and express the degree of their similarity. To support these comparisons we have developed a set of mobility state transition (MST) cost metrics that express dissimilarity as a function of differences in state. In the full paper we present these models in detail, and proceed with experimental results to demonstrate their use in spatiotemporal analysis.