Multi-resolution visualization of massive urban buildings is one of the most important components for a cyber city. Urban is a highly humanized system, and thus visualizing urban buildings needs to abide by people's habits of cognizing spatial relations between objects for their accurate and quick understanding of urban spatial information. This article proposes an approach to generalize and render urban building models in the context of Gestalt psychology and urban legibility. We introduce a new distance measurement method as the distance metric for the single-link clustering algorithm, which is used to group building footprints into clusters. Each cluster is merged based on the Delaunay triangulation and the polyline generalization algorithm. We then construct a hierarchical tree to store multi-resolution building models and implement interactive three-dimensional visualization of large-scale and high-density urban buildings. Experimental results indicate that the proposed methodology not only reduces the geometric complexity of urban models but also preserves urban legibility successfully and follow Gestalt principles.
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massive urban models;
Document Type: Research Article
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing, PR China
PTV America, Inc., Tacoma, WA, USA
Publication date: February 1, 2011
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