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.
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Acknowledgement
This work was funded by grants from the French program «investissement d’avenir» managed by the Agence Nationale de la Recherche of the French government (ANR), the European Commission (Auvergne FEDER funds) and «Région Auvergne» in the framework of LabEx IMobS 3 (ANR-10- LABX-16-01). We also acknowledge the support received from the Agence Nationale de la Recherche of the French government through the program «investissement d’avenir» 16-IDEX-0001 CAP 20-25 on the general topic of this paper.
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En-Nejjary, D., Pinet, F., Kang, MA. (2019). A Method to Improve the Performance of Raster Selection Based on a User-Defined Condition: An Example of Application for Agri-environmental Data. In: Corrales, J., Angelov, P., Iglesias, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change II. AACC 2018. Advances in Intelligent Systems and Computing, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-030-04447-3_13
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