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

Krämer, Michel ; Gutbell, Ralf ; Würz, Hendrik M. ; Weil, Jannis (2020)
Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization.
Proceedings of the 23rd AGILE Conference on Geographic Information Science.
doi: 10.5194/agile-giss-1-10-2020
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (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-andconquer 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.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2020
Autor(en): Krämer, Michel ; Gutbell, Ralf ; Würz, Hendrik M. ; Weil, Jannis
Art des Eintrags: Bibliographie
Titel: Scalable processing of massive geodata in the cloud: generating a level-of-detail structure optimized for web visualization
Sprache: Englisch
Publikationsjahr: 2020
Titel der Zeitschrift, Zeitung oder Schriftenreihe: AGILE: GIScience Series
Band einer Reihe: Vol.1
Veranstaltungstitel: Proceedings of the 23rd AGILE Conference on Geographic Information Science
DOI: 10.5194/agile-giss-1-10-2020
URL / URN: https://agile-giss.copernicus.org/articles/1/10/2020/
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Kurzbeschreibung (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-andconquer 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.

Freie Schlagworte: Distributed systems, Algorithms, Cloud computing, Geographic information systems (GIS)
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 22 Jul 2020 06:51
Letzte Änderung: 22 Jul 2020 06:51
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