Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Flow Modeling and Rendering to Support 3D River Shipping Based on Cross-Sectional Observation Data

Bibliographic Data

ID22033092
AuthorsXuequan Zhang (0000-0002-8755-2443, Wuhan University), Jin Liu (0000-0001-5816-2422, Wuhan University, corresponding author), Zihe Hu (0000-0001-6936-4759, Wuhan University), Ming Zhong (0000-0002-6107-5693, Wuhan University of Technology)
Year2020
Volume9
Issue3
Pages156
Publication date2020-03-09
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9030156
OpenAlexW3009078246
LanguageEN
References cited42

The flow in meandering rivers is characterized by rapid changes in flow velocity and water level, especially in flooded environments. Accurate cross-sectional observation data enable continuous monitoring of flow conditions, which is important for river navigation. In this paper, cross-sectional data based flow modeling and rendering methods are studied to build an interactive hybrid flow environment for three-dimensional river shipping. First, the sparse cross-sectional data are extrapolated and interpolated to provide dense sampling points. Then, the data are visualized separately by dynamic texture mapping, particle tracking, streamline rendering, and contour surface rendering. Finally, the rendering models are integrated with ship animation to build a comprehensive hybrid river navigation scenario. The proposed methods are tested by visualizing measured cross-sectional data in the Yangtze River using an open-source software, called World Wind. The experimental results demonstrate that the hybrid flow rendering achieves comprehensive visual effect and the rendering frame rate is greater than 30. The interactive hybrid flow visualization is beneficial to support river shipping analysis

Visualization · Computer Graphics and Visualization Techniques · Computer Science · Cryospheric studies and observations · Hydrology and Sediment Transport Processes · Artificial Intelligence

Highly citedNo

Tools

Open DOISci-HubOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae