GeoCubes Finland has been developed in the context of the Finnish Open Geospatial Information Infrastructure for Research (oGIIR) initiative. The oGIIR is an open-access virtual infrastructure supporting the broad multidisciplinary scientific research community by offering geospatial data services, scalable geocomputing services and a knowledge-sharing network. The oGIIR is jointly developed by the Finnish Geospatial Research Institute (FGI) in the National Land Survey of Finland (NLS), the University of Turku, Aalto University, the University of Eastern Finland, the Finnish Environment Institute (SYKE), the Geological Survey of Finland (GTK), the Natural Resources Institute Finland (LUKE) and CSC – IT Center for Science. GeoCubes Finland is a cached storage of geospatial data for supporting the needs of the Finnish research community.
GeoCubes is a unified, multi-resolution repository of raster-formatted geospatial data. GeoCubes aims at facilitating spatial analysis processes by providing interoperable data sets that have been pre-processed for easy access and integration. GeoCubes contains a representative selection of Finnish geospatial data sets with national coverage. The contained data sets are transformed into a common two-dimensional grid and into a unified set of resolution levels. A set of access protocols are supported for accessing the contents of GeoCubes in order to facilitate utilisation in various client applications.
The contents of GeoCubes Finland include a representative selection of spatial data sets maintained by governmental research organisations in Finland (like SYKE, LUKE and GTK). As reference data, some general-purpose data sets provided by the NLS are also included. Data sets are organised as individual layers of information with common representational properties for easy integration and analysis. Examples of data sets to be stored in GeoCubes in the first phase include high-resolution elevation models and surface models (from the NLS), land-use layers (from the SYKE), soil map layers (from the GTK) and national forest inventory layers (from the LUKE).
The standardised grid applied in GeoCubes is based on the Finnish national Coordinate Reference System (CRS) ETRS-TM35FIN (EPSG code 3067). This projected CRS is compatible with the pan-European ETRS89 system. ETRS-TM35FIN covers the whole country in one projection zone and has the false easting value of 500000 m on its central meridian at 27°E longitude. The origin of the GeoCubes Finland’s grid (top-left corner) is located at the coordinate point (0, 7800000). The easting value of the origin is selected to avoid negative coordinates. The northing coordinate value is selected as a round 100 km value, allowing for good coverage of the country.
GeoCubes Finland applies the following resolution levels: 1, 2, 5, 10, 20, 50, 100, 200, 500, and 1000 m. The resolution levels applicable for a given source data essentially depend on the properties, like spatial accuracy, of the data set. Round resolution values, rather than the traditionally used exponents of two in image pyramids, are selected to facilitate integration with external sources (like statistical data sets) and to follow the values commonly used in spatial analysis reporting.
For easy transfer and processing of the GeoCubes Finland data sets, the content is subdivided in 100 km * 100 km blocks with a round 100 km origin (top-left corner) coordinate values. The territory of Finland can be covered with 60 such blocks. The so-called virtual raster mechanism is used to treat the 60 individual files as a one continuous data set.
GeoCubes Finland data storage is implemented in the form of Cloud-Optimized GeoTIFF (COG) files (TIFF: Tagged Image File Format), each representing a 100 km * 100 km block. The resolution levels are stored both as internal GeoTIFF overview layers and as individual resolution-specific GeoTIFF files. The set of 60 blocks is aggregated into a single content representation by using the GDAL's (Geospatial Data Abstraction Library) Virtual format (VRT) mechanism. VRT files also combine together the files on individual resolution levels. GDAL's Python API is extensively used in the data ingestion and data provision procedures.