3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

Zhang, Wuming; Wang, Hongtao; Chen, Yiming; Yan, Kai; Chen, Mei

doi:10.3390/rs6098107

Cited by 34 publications

(17 citation statements)

References 38 publications

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“…Recent advances in Light Detecting and Ranging (LiDAR) and unmanned aerial vehicle technology greatly increase the availability of vertical dimensional data in an affordable way [52][53][54][55]. These advances will greatly facilitate the quantitative analysis of the vertical structure of the urban landscapes and their social and ecological impacts.…”

Section: Discussionmentioning

confidence: 99%

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

et al. 2017

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Landscape changes associated with urbanization can lead to many serious ecological and environmental problems. Quantifying the vertical structure of the urban landscape and its change is important to understand its social and ecological impacts, but previous studies mainly focus on urban horizontal expansion and its impacts on land cover/land use change. This papers focuses on the residential landscape to investigate how the vertical dimension of the urban landscape (i.e., building height) change through time, and how such change is related to changes in the horizontal dimension of the landscape, using Beijing, the capital of China, as a case study. We quantified the expansion of the residential neighborhoods from 1949 to 2009, and changes in vegetation coverage, building density, and building height within these neighborhoods, using 1 m spatial resolution imagery. One-way ANOVA and correlation analysis were used to examine the relationships of building height to vegetation coverage and building density. We found: (1) The residential areas expanded rapidly and were dominated by outward growth, with much less within-city infilling. The growth rate varied greatly through time, first increasing from 1949-2004 and then decreasing from [2005][2006][2007][2008][2009]. The expansion direction of newly built residential neighborhoods shifted from west to north in a clockwise direction. (2) The vertical structure of residential neighborhoods changed with time and varied in space, forming a "low-high" pattern from urban central areas to the urban edges within the 5th ring road of Beijing. (3) The residential neighborhoods built in different time periods had significant differences in vegetation coverage, building density, and building height. The residential neighborhoods built in more recent years tended to have taller buildings, lower building density and lower vegetation coverage.

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Section: Discussionmentioning

confidence: 99%

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

et al. 2017

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“…As a result, the compound buildings can be decomposed into some parametric primitives (e.g., gabled roof, half-hipped roof, and hipped roof). Then, each parametric primitive is reconstructed by a primitive-based building modeling method proposed by Zhang et al [41], which was developed to generate basic building primitives using constraints from LiDAR data and aerial imagery. In contrast to the aforementioned reconstruction methods using roof topology graph, the main contribution of this paper can be summarized as follows: (1) automatic decomposition of compound buildings into semantic primitives by using an attributed graph.…”

Section: Introductionmentioning

confidence: 99%

Semantic Decomposition and Reconstruction of Compound Buildings with Symmetric Roofs from LiDAR Data and Aerial Imagery

et al. 2015

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3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify this problem, this article proposes a novel approach to automatically decompose the compound buildings with symmetric roofs into semantic primitives by exploiting local symmetry contained in the building structure. In this approach, the proposed decomposition allows the overlapping of neighbor primitives and each decomposed primitive can be represented as a parametric form, which simplify the complexity of the building reconstruction and facilitate the integration of LiDAR data and aerial imagery into a parameters optimization process. The proposed method starts by extracting isolated building regions from the LiDAR point clouds. Next, point clouds belonging to each compound building are segmented into planar patches to construct an attributed graph, and then the local symmetries contained in the attributed graph are exploited to automatically decompose the compound buildings into different semantic primitives. In the final step, 2D image features are extracted depending on the initial 3D primitives generated from LiDAR data, and then the compound building OPEN ACCESSRemote Sens. 2015, 7 13946 is reconstructed using constraints from LiDAR data and aerial imagery by a nonlinear least squares optimization. The proposed method is applied to two datasets with different point densities to show that the complexity of building reconstruction can be reduced considerably by decomposing the compound buildings into semantic primitives. The experimental results also demonstrate that the traditional model driven methods can be further extended to the automated reconstruction of compound buildings by using the proposed semantic decomposition method.

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“…cf. (Zhang et al, 2014, Haala and Kada, 2010, SinningMeister et al, 1996), such as the width of a building, length of ridges and eaves, and roof height. The first part of the engine, described in this section, creates features described by these parameters, using an encoded grammar and a set of rules.…”

Section: Parametric Description and Rulesmentioning

confidence: 99%

Generation of Multi-Lod 3d City Models in Citygml With the Procedural Modelling Engine Random3dcity

Biljecki

Ledoux

Stoter

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The production and dissemination of semantic 3D city models is rapidly increasing benefiting a growing number of use cases. However, their availability in multiple LODs and in the CityGML format is still problematic in practice. This hinders applications and experiments where multi-LOD datasets are required as input, for instance, to determine the performance of different LODs in a spatial analysis. An alternative approach to obtain 3D city models is to generate them with procedural modelling, which is-as we discuss in this paperwell suited as a method to source multi-LOD datasets useful for a number of applications. However, procedural modelling has not yet been employed for this purpose. Therefore, we have developed RANDOM3DCITY, an experimental procedural modelling engine for generating synthetic datasets of buildings and other urban features. The engine is designed to produce models in CityGML and does so in multiple LODs. Besides the generation of multiple geometric LODs, we implement the realisation of multiple levels of spatiosemantic coherence, geometric reference variants, and indoor representations. As a result of their permutations, each building can be generated in 392 different CityGML representations, an unprecedented number of modelling variants of the same feature. The datasets produced by RANDOM3DCITY are suited for several applications, as we show in this paper with documented uses. The developed engine is available under an open-source licence at Github at http://github.com/tudelft3d/Random3Dcity.

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3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

Cited by 34 publications

References 38 publications

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

Semantic Decomposition and Reconstruction of Compound Buildings with Symmetric Roofs from LiDAR Data and Aerial Imagery

Generation of Multi-Lod 3d City Models in Citygml With the Procedural Modelling Engine Random3dcity

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