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Object-oriented spatio-temporal model is directed by human cognition that each object has what/where/when attributes.
The precise and flexible structure of such models supports multi-semantics of space and time. This paper reviews current
research of spatio-temporal models using object-oriented approach and proposed a new spatio-temporal model based on
segmentation in order to resolve the updating problem of some special GIS system by taking advantages of object-oriented
spatio-temporal model and adopting category theory. Category theory can be used as a unifying framework for
specifying complex systems and it provides rules on how objects may be joined. It characterizes the segments of object
through mappings between them. The segment-oriented spatio-temporal model designed for urban panoramic maps is
described and implemented. We take points and polylines as objects in this model in the management of panoramic map
data. For the randomness of routes which transportation vehicle adopts each time, road objects in this model are split into
some segments by crossing points. The segments still remains polyline type, but the splitting makes it easier to update
the panoramic data when new photos are captured. This model is capable of eliminating redundant data and accelerating
data access when panoramas are unchanged. For evaluation purpose, the data types and operations are designed and
implemented in PostgreSQL and the results of experiments come out to prove that this model is efficient and expedient
in the application of urban panoramic maps.
Haiting Li,Lifan Fei,Qingshan Peng, andYanhong Li
"Design and implementation of segment oriented spatio-temporal model in urban panoramic maps", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74922K (15 October 2009); https://doi.org/10.1117/12.837511
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Haiting Li, Lifan Fei, Qingshan Peng, Yanhong Li, "Design and implementation of segment oriented spatio-temporal model in urban panoramic maps," Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74922K (15 October 2009); https://doi.org/10.1117/12.837511