Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston

Yu, Bailang; Liu, Hongxing; Wu, Jianping; Lin, Wei-Ming

doi:10.1080/01431160802555846

Cited by 68 publications

(29 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This application may benefit from attributes such as the address and type of building for additional analyses [108], and it is being supported by an increasing number of software implementations [109,110]. Furthermore, some researchers use dense point clouds rather than semantic 3D city models (e.g., [111][112][113][114]). For a comprehensive overview of research on solar potential applications see the recent review of Freitas et al [115].…”

Section: Estimation Of the Solar Irradiationmentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

579

373

View full text Add to dashboard Cite

In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

show abstract

Section: Estimation Of the Solar Irradiationmentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

579

373

View full text Add to dashboard Cite

show abstract

“…The first step is to derive the normalized digital surface model (nDSM), which is a rasterized representation of relative height information about objects above the ground [54]. It is computed as the difference between the digital surface model (DSM) and the digital elevation model (DEM).…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…On the other hand, if the selected base building height H 0 is too low, some small nonbuilding objects like automobiles will be detected as buildings. Different threshold values have been chosen to extract building objects in previous studies, e.g., 3 m in Ma (2005), 3.5m in Yu et al (2010) and 4 m in Yu et al, 2009a andYu et al, 2009b. After experimenting with different threshold values we chose 3 m as the threshold value for our case study.…”

Section: Step2 Mean Shift Improvement Of Ndsmmentioning

confidence: 99%

“…urban design, planning and land management. Moreover, building density affects the dispersion of atmospheric pollutants (Theodoridis and Moussiopoulos, 2000), the access of sunlight and solar radiation (Lam, 2000;Miguet and Groleau, 2002;Oke,1988;Yu et al, 2009a), the interior temperatures of buildings (Mills, 1997), the surface thermal conditions (Streutker, 2003). Kubota et al (2008) investigated the relationship between the building density of a residential neighborhood and the average wind speed at pedestrian level.…”

Section: Introductionmentioning

confidence: 99%

Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

Charou

Gyftakis

Bratsolis

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and "straightness" of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.

show abstract

Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston

Cited by 68 publications

References 43 publications

Applications of 3D City Models: State of the Art Review

Applications of 3D City Models: State of the Art Review

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

Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

Contact Info

Product

Resources

About