dc.contributor.author | Johnson, Donald W. | en_US |
dc.contributor.author | Jankun-Kelly, T. J. | en_US |
dc.contributor.editor | Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk | en_US |
dc.date.accessioned | 2021-06-12T11:24:07Z | |
dc.date.available | 2021-06-12T11:24:07Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-3-03868-148-9 | |
dc.identifier.uri | https://doi.org/10.2312/envirvis.20211077 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/envirvis20211077 | |
dc.description.abstract | Analysis of overlapping spatial data sets is a challenging problem with tension between clearly identifying individual surfaces and exploring significant overlaps/conflicts. One area where this problem occurs is when dealing multiple flood scenes that occur in an area of interest. In order to allow easier analysis of scenes with multiple overlapping data layers, we introduce a visualization system designed to aid in the analysis of such scenes. It allows the user to both see where different data sets agree, and categorize areas of disagreement based on participating surfaces in each area. The results are stable with regard to render order and GPU acceleration via OpenCL allows interaction with large datasets with preprocessing dynamically. This interactivity is further enhanced by data streaming which allows datasets too large to be loaded directly onto the GPU to be processed. After demonstrating our approach on a diverse set of ensemble datasets, we provide feedback from expert users. | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | I.3.8 [Computer Graphics] | |
dc.subject | Applications | |
dc.subject | Visualization H.5.2 [Information Interfaces and Presentation] | |
dc.subject | User Interfaces | |
dc.subject | Graphical user interfaces | |
dc.title | GPU-Assisted Visual Analysis of Flood Ensemble Interaction | en_US |
dc.description.seriesinformation | Workshop on Visualisation in Environmental Sciences (EnvirVis) | |
dc.description.sectionheaders | Probabilistic and Uncertainty-based Techniques for Environmental Data Visualization | |
dc.identifier.doi | 10.2312/envirvis.20211077 | |
dc.identifier.pages | 1-8 | |