Scalable and queryable compressed storage structure for raster data

Previous compact data structures designed to store raster data work well when the raster dataset has few different values. Nevertheless, when the number of different values in the raster increases, their space consumption and search performance degrade. Our experiments show that our storage structure improves previous approaches in all aspects, especially when the number of different values is large, which is critical when applying over real datasets. Compared with classical methods for storing rasters, namely netCDF, our method competes in space and excels in access and query times.