XD
X. Du
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A vector tessellation method is proposed for grid pattern recognition in street networks. This study regards a street network as an independent subspace embedded in the 2D space, and subdivides street segments into linear elements with equal lengths. The characteristics of grid patterns are extracted, including directional, geometrical and topological features. To map the object space to the feature space and to build a vector field, the linear element is described as a feature vector and the eigenvalues are calculated with the neighboring elements. A grid pattern classification is realized based on a support vector machine (SVM), and the classification result is optimized based on Gestalt principles. The method was applied to the street network of Shenzhen. The experimental results show that the method effectively mines grid pattern in street networks.
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A vector tessellation method is proposed for grid pattern recognition in street networks. This study regards a street network as an independent subspace embedded in the 2D space, and subdivides street segments into linear elements with equal lengths. The characteristics of grid patterns are extracted, including directional, geometrical and topological features. To map the object space to the feature space and to build a vector field, the linear element is described as a feature vector and the eigenvalues are calculated with the neighboring elements. A grid pattern classification is realized based on a support vector machine (SVM), and the classification result is optimized based on Gestalt principles. The method was applied to the street network of Shenzhen. The experimental results show that the method effectively mines grid pattern in street networks.
Conference paper
(2016)
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Filip Biljecki, Hugo Ledoux, Xin Du, Jantien Stoter, Kean Huat Soon, Victor Khoo
To be used as input in most simulation and modelling software, 3D city models should be geometrically and topologically valid, and semantically rich. We investigate in this paper what is the quality of currently available CityGML datasets, i.e. we validate the geometry/topology of the 3D primitives (Solid and MultiSurface), and we validate whether the semantics of the boundary surfaces of buildings is correct or not. We have analysed all the CityGML datasets we could find, both from portals of cities and on different websites, plus a few that were made available to us. We have thus validated 40M surfaces in 16M 3D primitives and 3.6M buildings found in 37 CityGML datasets originating from 9 countries, and produced by several companies with diverse software and acquisition techniques. The results indicate that CityGML datasets without errors are rare, and those that are nearly valid are mostly simple LOD1 models. We report on the most common errors we have found, and analyse them. One main observation is that many of these errors could be automatically fixed or prevented with simple modifications to the modelling software. Our principal aim is to highlight the most common errors so that these are not repeated in the future. We hope that our paper and the open-source software we have developed will help raise awareness for data quality among data providers and 3D GIS software producers.
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To be used as input in most simulation and modelling software, 3D city models should be geometrically and topologically valid, and semantically rich. We investigate in this paper what is the quality of currently available CityGML datasets, i.e. we validate the geometry/topology of the 3D primitives (Solid and MultiSurface), and we validate whether the semantics of the boundary surfaces of buildings is correct or not. We have analysed all the CityGML datasets we could find, both from portals of cities and on different websites, plus a few that were made available to us. We have thus validated 40M surfaces in 16M 3D primitives and 3.6M buildings found in 37 CityGML datasets originating from 9 countries, and produced by several companies with diverse software and acquisition techniques. The results indicate that CityGML datasets without errors are rare, and those that are nearly valid are mostly simple LOD1 models. We report on the most common errors we have found, and analyse them. One main observation is that many of these errors could be automatically fixed or prevented with simple modifications to the modelling software. Our principal aim is to highlight the most common errors so that these are not repeated in the future. We hope that our paper and the open-source software we have developed will help raise awareness for data quality among data providers and 3D GIS software producers.