Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization

Michel Krämer, Ralf Gutbell, Hendrik M. Würz, Jannis Weil

Abstract: Articles | Volume 1 ArticleMetricsRelated articles Articles | Volume 1 https://doi.org/10.5194/agile-giss-1-10-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. https://doi.org/10.5194/agile-giss-1-10-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Articles | Volume 1 ArticleMetricsRelated articles 15 Jul 2020 | 15 Jul 2020 Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization Michel Krämer, Ralf Gutbell, Hendrik M. Würz, and Jannis Weil Michel Krämer × Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany Technical University of Darmstadt, Germany Ralf Gutbell × Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany Hendrik M. Würz × Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany Technical University of Darmstadt, Germany Jannis Weil × Technical University of Darmstadt, Germany Keywords: Distributed systems, Algorithms, Cloud computing, Geographic Information Abstract. We present a cloud-based approach to transform arbitrarily large terrain data to a hierarchical level-of-detail structure that is optimized for web visualization. Our approach is based on a divide-and-conquer strategy. The input data is split into tiles that are distributed to individual workers in the cloud. These workers apply a Delaunay triangulation with a maximum number of points and a maximum geometric error. They merge the results and triangulate them again to generate less detailed tiles. The process repeats until a hierarchical tree of different levels of detail has been created. This tree can be used to stream the data to the web browser. We have implemented this approach in the frameworks Apache Spark and GeoTrellis. Our paper includes an evaluation of our approach and the implementation. We focus on scalability and runtime but also investigate bottlenecks, possible reasons for them, as well as options for mitigation. The results of our evaluation show that our approach and implementation are scalable and that we are able to process massive terrain data. Download & links Article (PDF, 4803 KB) Download & links Article (4803 KB) Metadata XML BibTeX EndNote Share How to cite. Krämer, M., Gutbell, R., Würz, H. M., and Weil, J.: Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization, AGILE GIScience Ser., 1, 10, https://doi.org/10.5194/agile-giss-1-10-2020, 2020.