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/ |
| Zugehörige Links: | |
| 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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