Extending General Compact Querieable Representations to GIS Applications

TL;DR

This paper introduces new compact data structures for efficiently storing and querying raster data in main memory, significantly reducing space usage while maintaining query performance in GIS applications.

Contribution

It extends general compact queryable representations to raster data, providing novel structures for binary, general, and time-evolving raster datasets.

Findings

Structures are up to 10 times smaller than linear quadtrees.

Achieve space comparable to non-querieable representations.

Efficiently support typical raster queries.

Abstract

The raster model is commonly used for the representation of images in many domains, and is especially useful in Geographic Information Systems (GIS) to store information about continuous variables of the space (elevation, temperature, etc.). Current representations of raster data are usually designed for external memory or, when stored in main memory, lack efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We present different representations for binary raster data, general raster data and time-evolving raster data. We experimentally compare our proposals with traditional storage mechanisms such as linear quadtrees or compressed GeoTIFF files. Results show that our structures are up to 10 times smaller than classical linear quadtrees, and even comparable in space to non-querieable representations of raster data, while efficiently answering a number of typical queries.