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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 Martin; Weil, Jannis

Fulltext urn:nbn:de:0011-n-5968511 (4.6 MByte PDF)
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Created on: 23.7.2020

Partsinevelos, Panagiotis (Ed.) ; Association of Geographic Information Laboratories in Europe -AGILE-:
23rd AGILE Conference on Geographic Information Science 2020. Online resource : Chania, Greece, 16th - 19th June of 2020
Göttingen: Copernicus Publications, 2020 (AGILE: GIScience Series 1)
20 pp.
Conference on Geographic Information Science <23, 2020, Online>
Conference Paper, Electronic Publication
Fraunhofer IGD ()
Lead Topic: Visual Computing as a Service; Research Line: Computer graphics (CG); distributed systems; algorithms; Cloud Computing; geographic information systems (GIS)

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.