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Top1. Introduction
The current advances in sensor technology, remote sensing and computer techniques have led to the production of large volumes of spatial data. Sensors are now smaller, cheaper and even smart (Melesse et al., 2007). Increasingly more geo-referenced sensors are deployed for many applications, such as environmental monitoring, precision agriculture, positioning, etc. Remote sensing and spatiotemporal simulation also produce large geo-referenced datasets. The generation of spatiotemporal data at a large scale and in a high-resolution leads to the development of new techniques to manage the volumes of produced data (Prasad et al., 2015). Many of these data take the form of raster sets. A raster is a geo-referenced 2-dimensional array in which each cell is associated with a value. The cells of a raster can be represented by pixels where the colors correspond to different values of a measure, such as temperature, vegetation density, CO2 measurements, etc. (Kang et al., 2015). Data availability and data storage are often no longer barriers, whereas the real bottleneck is, in many cases, the analysis of these spatial data that continue to grow dramatically (Barbian and Assunção, 2017). Map algebra is one of the usual raster analysis techniques. It is a set of conventions, capabilities and techniques to process rasters (Pullar, 2001). Map algebra allows for the defining of how a set of rasters can be aggregated, and thus, it proposes different types of functions that usually have one or several rasters as inputs and that returns one raster or one indicator as a result (Tomlin, 1994). This technique can be used to produce a summary of a set of rasters. For example, the author of (Kang et al., 2015) shows a method to aggregate a set of rasters representing geo-referenced air quality values over time in order to produce a single raster summarizing the air quality for a determined period of time. In (Kang et al., 2015), using a data warehouse, users can access a raster summary instead of browsing all the rasters. There is one raster for every 15-minute time period and for one pollutant. Every raster in the data set is associated to the same spatial region. As shown below, users visualize raster summary aggregated according to different dimension (time, pollutant family, etc.).
Table 1.
Visualize raster summary according to different dimension
When the size of the raster and the set of raster data are small, the map algebra operation executions are fast since the functions of map algebra are usually based on simple arithmetic operations (addition, subtraction, minimum, maximum, average, etc.). However, when a large raster dataset is used, it is important to optimize the raster computation to obtain a reasonable execution time. As raster processing is often highly parallelizable, one method to improve the performance is to use a Graphics Processing Unit (GPU).