View-based greenery: A three-dimensional assessment of city buildings’ green visibility using Floor Green View Index

Yu, Siyi; Yu, Bo; Song, Wei; Wu, Bin; Zhou, Jianhua; Huang, Yan; Wu, Jianping; Zhao, Feng; Wei-qing, Mao

doi:10.1016/j.landurbplan.2016.04.004

Cited by 111 publications

(59 citation statements)

References 39 publications

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“…As explained in Section 1, some existing approaches use point clouds and images to extract vegetation [15][16][17]. The image requires light source and the brightness of vegetation of the images may depend on the species and measurement season.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data

Susaki¹,

Kubota²

2017

Remote Sensing

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Abstract:In this paper, we propose a method for using mobile laser-scanning data to estimate the green space ratio (GSR), a landscape index that represents the proportion of green area to the whole-view area. The proposed method first classifies and segments vegetation using voxel-based and shape-based approaches. Vertical planar-surface objects are excluded, and randomly distributed objects are extracted as vegetation via multi-spatial-scale analysis. Then, the method generates a map representing occlusion by vegetation, and estimates GSR at an arbitrary location. We applied the method to a data set collected in a residential area in Kyoto, Japan. We compared the results with the ground truth data and obtained a root mean squared error of approximately 4.1%. Although some non-vegetation with rough surfaces was falsely extracted as vegetation, our method seems to estimate GSR to an acceptable accuracy.

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Section: Discussionmentioning

confidence: 99%

“…Yang et al [16] estimated Green View index using field survey data and photographs. Yu et al [17] presented the Floor Green View Index, an indicator defined as the area of visible vegetation on a particular floor of a building. This index is calculated from airborne LiDAR data and aerial near-infrared photographs to calculate NDVI.…”

Section: Introductionmentioning

confidence: 99%

Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data

Susaki¹,

Kubota²

2017

Remote Sensing

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“…It is computed as the difference between the digital surface model (DSM) and the digital elevation model (DEM). In our study, the DSM was generated from the airborne LiDAR point clouds by using the linear triangulated irregular network (TIN) interpolation method [23][24][25][26]. The DEM was then interpolated from the ground points which was classified using a progressive morphological filter [55].…”

Section: Building Contours Generationmentioning

confidence: 99%

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

et al. 2017

Remote Sensing

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3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds.

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“…Several spatial analyses appear to derive ambiguous results, notwithstanding the challenges of how to quantify them. For instance, 3D city models may be used for different kinds of visibility analyses, and therefore quantified in different ways: binary (a point in space is visible or not), distance (range) of visibility, the area or volume visible from a point, number of buildings that have visual access to a feature, and population that has visual access to a point (Cervilla, Tabik, Vías, Mérida, & Romero, 2016;Grassi & Klein, 2016;Wrózyński, Sojka, & Pyszny, 2016;Yu et al, 2016). Each one has different error propagation behavior (Biljecki et al, 2017).…”

Section: Selection Of the Spatial Analysesmentioning

confidence: 99%

The effect of acquisition error and level of detail on the accuracy of spatial analyses

Biljecki

Heuvelink

Ledoux

et al. 2017

Cartography and Geographic Information Science

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There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data. ARTICLE HISTORY

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View-based greenery: A three-dimensional assessment of city buildings’ green visibility using Floor Green View Index

Cited by 111 publications

References 39 publications

Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data

Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data

A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds

The effect of acquisition error and level of detail on the accuracy of spatial analyses

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