Abstract

Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast systems to monitoring of forest degradation or soil contamination. Many different sensors are generating this type of data, which makes such analyses possible, but also challenges the technological capacity to store and retrieve the data. In this work, we propose a space-efficient representation of raster time series that is based on Compact Data Structures (CDS). Our method uses a strategy of snapshots and logs to represent the data, in which both components are represented using CDS. We study two variants of this strategy, one with regular sampling and another one based on a heuristic that determines at which timestamps should the snapshots be created to reduce the space redundancy. We perform a comprehensive experimental evaluation using real datasets. The results show that the proposed strategy is competitive in space with alternatives based on pure data compression, while providing much more efficient query times for different types of queries.