Querying streaming geospatial image data: the geostreams project
Quinn J. Hart, Michael Gertz · 2005
Data products generated from remotely-sensed, geospatial imagery (RSI) used in emerging areas, such as global climatology, environmental monitoring, land use, and disaster management, require costly and time consuming efforts in processing the data. For the researcher, data is typically fully replicated using file-based approaches, then undergoes multiple processing steps, these steps often being duplicated at many sites. For the provider, data distribution is often tied directly to the data archiving task, focusing on simple, coarse grained offerings. Many RSI instruments transmit data in a continuous or semi-continuous stream, but current techniques in processing do not utilize the stream nature of the imagery. Recent research on continuous querying of data streams offer alternative processing approaches, but typically assume tuple style data objects, relying on traditional relational models as basis for query processing techniques and architectures. Complex types of stream objects, such as multidimensional data sets or raster image data, have not been considered. Our project, GeoStreams, is a framework to process multiple continuous queries against streaming remotely-sensed geospatial image data. This paper introduces the basic features underlying the GeoStreams model. We describe some interesting aspects in processing streaming image data, including optimization and evaluation using specialized index structures. Remotely sensed data, in particular satellite imagery, play an important role in many environmental applications and models [10]. Simple, convenient access to remote sensing data has traditionally been a barrier to research and applications. The huge amounts of data generated by the Earth Observing System (EOS) platforms have precipitated a change in this scenario, and access to data products has become substantially easier. New EOS data archives offer fine examples of more transparent data access. However, access to this imagery still largely centers on choosing coarse grained, standard data products for specific regions