Earth observation sciences, astronomy, and seismology have large data sets which have inherently rich spatial and geospa-tial information. In combination with large collections of semantically rich objects which have a large number of thematic properties, they form a new source of knowledge for urban planning, smart cities and natural resource management. Modeling and storing these properties indicating the relationships between them is best handled in a relational database. Furthermore, the scalability requirements posed by the latest 26-attribute light detection and ranging (LI-DAR) data sets are a challenge for file-based solutions. In this demo we show how to query a 640 billion point data set using a column store enriched with GIS functionality. Through a lightweight and cache conscious secondary index called Imprints, spatial queries performance on a flat table storage is comparable to traditional file-based solutions. All the results are visualised in real time using QGIS.
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