Spatio-temporal aggregates over raster image data

ABSTRACT

Spatial, temporal and spatio-temporal aggregates over continuous streams of remotely sensed image data build a fundamental operation in many applications in the environmental sciences. Several approaches to efficiently compute multi-dimensional aggregates have been proposed in the literature. However, none of these approaches is suitable to compute aggregate values over streaming raster image data where the spatial extents and positions of individual images vary over time. In particular, the computation of a single aggregate value becomes less meaningful when the image data contribute only partially to a query region.

In this paper, we present an indexing scheme -- based on the Box-Aggregation Tree -- to efficiently compute spatio-temporal aggregates over streams of raster image data that vary in position and size. Using information about the spatial extent of incoming image data, we show how multiple aggregate values are computed for a single spatio-temporal query, thus providing more meaningful query results over spatially varying image data. Using National Oceanic and Atmospheric Administration's (NOAA) Geostationary Operational Environmental Satellite (GOES) data, we show the feasibility and efficiency of the proposed approach.

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Index Terms

• Human-centered computing

  • Visualization

    • Visualization application domains

      • Geographic visualization

• Information systems

  • Data management systems

    • Data structures

      • Data access methods

  • Information systems applications

    • Spatial-temporal systems