2013
DOI: 10.1002/joc.3868
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Three-dimensional point cloud based sky view factor analysis in complex urban settings

Abstract: Quantitatively describing effects caused by trees is a challenging issue for sky view factor (SVF) studies. The complex geometrical shapes of trees and the seasonally changing canopy volume caused by leaf growth and defoliation have forced SVF users to disregard trees in their analyses or to apply a simple geometric object, such as a rectangular pole or vertically elongated ellipsoid. The three-dimensional point cloud (3DPC) method is useful for quantitative analysis of urban settings by describing the structu… Show more

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Cited by 26 publications
(6 citation statements)
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“…In general, quantitatively describing the impact of trees is a challenging issue in SVF studies. The complex geometrical shapes of trees and the seasonal changes in crown volume caused by leaf growth and falling leaves force researchers to ignore trees or apply simple geometric objects [47]. Therefore, SVI will still be employed as a data source, studying to further quantify the effect of trees on the accuracy of the SVF and to remove trees based on the context of the image in the future.…”
Section: Discussionmentioning
confidence: 99%
“…In general, quantitatively describing the impact of trees is a challenging issue in SVF studies. The complex geometrical shapes of trees and the seasonal changes in crown volume caused by leaf growth and falling leaves force researchers to ignore trees or apply simple geometric objects [47]. Therefore, SVI will still be employed as a data source, studying to further quantify the effect of trees on the accuracy of the SVF and to remove trees based on the context of the image in the future.…”
Section: Discussionmentioning
confidence: 99%
“…The skylight received by a point can be modeled using the sky-view factor a, which ranges from 0 to 1 and represents the proportion of skylight relative to a horizontal plane. It can be calculated using a variety of methods [24]- [26], including the portion de ciel visible (PCV) plugin in CloudCompare [27].…”
Section: Methodsmentioning
confidence: 99%
“…Moreover, the amount of memory required by our model is more than a thousand times lower. By combining our model with different methods for obtaining the skyline from 3D urban point clouds 36,37 , it is possible to quickly generate a solar energy potential map. Considering the above-mentioned simulation times, the calculation of the potential of a PV system over a 100 m by 100 m cadastre with 1 m resolution using the irradiance-based approach would take 43 minutes, whereas our method could do it in only 2 minutes.…”
Section: Improvement In Computation Timementioning
confidence: 99%