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Efficient Semantic Enrichment Process for Spatiotemporal Trajectories in Geospatial Environment

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12318)


The existing semantic enrichment process approaches which can produce semantic trajectories, are generally time consuming. In this paper, we propose a semantic enrichment process framework for spatiotemporal trajectories in geospatial environment. It can derive new semantic trajectories through the three phases: pre-annotated semantic trajectories storage, spatiotemporal similarity measurement, and semantic information matching. Having observed the common trajectories in the same geospatial object scenes, we propose an algorithm to match semantic information in pre-annotated semantic trajectories to new spatiotemporal trajectories. Finally, we demonstrate the effectiveness and efficiency of our proposed approach by using the real dataset.


  • Semantic trajectories
  • Semantic enrichment process
  • Semantic information matching

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  • DOI: 10.1007/978-3-030-60290-1_27
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This work is supported by the National Natural Science Foundation of China No. 41971343 and NSFC.61702271.

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Correspondence to Bin Zhao .

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Han, J., Liu, M., Ji, G., Zhao, B., Liu, R., Li, Y. (2020). Efficient Semantic Enrichment Process for Spatiotemporal Trajectories in Geospatial Environment. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham.

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