A Method to Improve the Performance of Raster Selection Based on a User-Defined Condition: An Example of Application for Agri-environmental Data

Abstract

More and more environmental and agricultural data are now acquired with a high precision and temporal frequency. These data are often represented in the form of rasters and are useful for agricultural activities or climate change analyses. In this paper, we propose a new method to process very large raster. We present a new technique to improve the execution time of the selection and calculation of data summaries (e.g., the average temperature for a region) on a temporal sequence of rasters. We illustrate the use of our approach on the case of temperature data, which is important information both for agriculture and for climate change analyses. We have generated several data sets in order to analyze the influence of the different value properties on the process performance. One of our final goals is to provide information about the value conditions in which the proposed processing should be used.

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

“…Furthermore, our methods using the sorting step enhance the results, especially, for the parallel version. Thus, a significant speed up is obtained (4,8) for the dataset 4 for watermark 2 and humidity.…”

“…In this subsection, we overcome the limitation of the straightforward parallel approach by introducing a sorting step in the process. We have introduced the main principle for the individual selection of rasters in [8,28]. The idea behind the sorting is to try to reject the subsequences not satisfying the query condition in the early stages to avoid useless computations, since the goal of the query is not to compute the average of all subsequences.…”

“…Hence, several kinds of functions are proposed having as an input one or many rasters and as an output one raster or one indicator [7]. For example, the authors in [8] use the average and the addition operations in order to select rasters based on a user-defined condition. In addition, map algebra can also be used to produce raster data summaries [9].…”

“…Second, we propose a sophisticated rejection heuristic in order to reject the unwanted subsequences in the early stages and as a result, the useless, heavy computations are avoided. We have introduced the idea of early data rejection in a single raster in [8]. Finally, the proposed methods are tested on a real dataset and can be used for other types of data that have the same characteristics and can be extended to other operations.…”