Abstract
Three-dimensional (3D) building models have been widely used in the fields of urban planning, navigation and virtual geographic environments. These models incorporate many details to address the complexities of urban environments. Level-of-detail (LOD) technology is commonly used to model progressive transmission and visualization. These detailed groups of models can be replaced by a single model using generalization. In this paper, the texture features are first introduced into the generalization process, and a self-organizing mapping (SOM)-based algorithm is used for texture classification. In addition, a new cognition-based hierarchical algorithm is proposed for model-group clustering. First, a constrained Delaunay triangulation (CDT) is constructed using the footprints of building models that are segmented by a road network, and a preliminary proximity graph is extracted from the CDT by visibility analysis. Second, the graph is further segmented by the texture–feature and landmark models. Third, a minimum support tree (MST) is created from the segmented graph, and the final groups are obtained by linear detection and discrete-model conflation. Finally, these groups are conflated using small-triangle removal while preserving the original textures. The experimental results demonstrate the effectiveness of this algorithm.
Highlights
Virtual Geographic Environments (VGEs) have been proposed as a new generation of geographic analysis tools to improve our understanding of the geographic world and assist in solving geographic problems at a deeper level [1,2]
Cities are the centers of living; people mainly work and live in cities; three-dimensional urban-building models are key elements of cities that have been widely applied in urban planning, navigation, virtual geographic environments and other fields [3,4,5,6,7,8]
Generation of an minimum support tree (MST) from the segmented graph based on the nearest distance, followed by linear detection and discrete-polygon conflation, which originate from Gestalt theory; (5) the conflation of the grouping models and regeneration of the texture; and (6) the visualization of these models
Summary
Virtual Geographic Environments (VGEs) have been proposed as a new generation of geographic analysis tools to improve our understanding of the geographic world and assist in solving geographic problems at a deeper level [1,2]. Guercke et al [20] expressed different aspects of the aggregation of building models in the form of mixed-integer-programming problems He et al [21] proposed a footprint-based generalization approach for 3D building groups in the context of city visualization by converting 3D generalization tasks into 2D issues via buildings’ footprints while reducing both the geometric complexity and information density. Zhang et al [22] proposed a spatial cognition analysis technique for building displacement and deformation in city maps based on Gestalt and urban-morphology principles These approaches are mainly based on geometric characteristics. This paper is the first to introduce texture features into 3D-model-group clustering and generalization based on Gestalt and urban-morphology principles.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
