The application of GIS and 3D graphic software to visual impact assessment of wind turbines

Wróżyński, Rafał; Sojka, Mariusz; Pyszny, Krzysztof

doi:10.1016/j.renene.2016.05.016

Cited by 52 publications

(22 citation statements)

References 32 publications

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“…Open-StreetMap (Uden and Zipf, 2013;Noskov and Doytsher, 2013;Goetz, 2013;Fan et al, 2014;Fan and Zipf, 2016). In fact, we have found a substantial number of occurrences of 3D models generated by simply extruding a footprint to the height obtained by multiplying the number of storeys with an assumed storey height (Coors et al, 2009;Şengül, 2010;Buhur et al, 2009;Yu et al, 2007;Coors, 2003;Guney et al, 2012;Sugihara and Shen, 2016;Wróżyński et al, 2016;Nichol and Wong, 2005;Ratti and Richens, 2004;Schläpfer et al, 2016;Perez et al, 2013;Teo and Cho, 2016;Santana et al, 2017). The storey height seems to vary among papers, ranging from 2.8 and 3.5 m (Kurakula and Kuffer, 2008;Vermeulen et al, 2015;Guney et al, 2012;Goetz and Zipf, 2012).…”

Section: Using Attributes (Number Of Storeys)mentioning

confidence: 88%

“…The storey height seems to vary among papers, ranging from 2.8 and 3.5 m (Kurakula and Kuffer, 2008;Vermeulen et al, 2015;Guney et al, 2012;Goetz and Zipf, 2012). Models constructed in this way have proven useful in a variety of applications, such as visibility analyses and energy simulations (Wróżyński et al, 2016;Perez et al, 2013). To a lesser extent, some of the obtained 3D models are used for interactive querying, e.g.…”

Section: Using Attributes (Number Of Storeys)mentioning

confidence: 99%

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Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

121

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Elevation datasets (e.g. point clouds) are an essential but often unavailable ingredient for the construction of 3D city models. We investigate in this paper to what extent can 3D city models be generated solely from 2D data without elevation measurements. We show that it is possible to predict the height of buildings from 2D data (their footprints and attributes available in volunteered geoinformation and cadastre), and then extrude their footprints to obtain 3D models suitable for a multitude of applications. The predictions have been carried out with machine learning techniques (random forests) using 10 different attributes and their combinations, which mirror different scenarios of completeness of real-world data. Some of the scenarios resulted in surprisingly good performance (given the circumstances): we have achieved a mean absolute error of 0.8 m in the inferred heights, which satisfies the accuracy recommendations of CityGML for LOD1 models and the needs of several GIS analyses. We show that our method can be used in practice to generate 3D city models where there are no elevation data, and to supplement existing datasets with 3D models of newly constructed buildings to facilitate rapid update and maintenance of data.

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Section: Using Attributes (Number Of Storeys)mentioning

confidence: 88%

Section: Using Attributes (Number Of Storeys)mentioning

confidence: 99%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

121

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show abstract

“…The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W8, 2017 5th International Workshop LowCost 3D -Sensors, Algorithms, Applications, 28-29 November 2017, Hamburg, Germany presented by Czyńska and Rubinowicz (2017), Rubinowicz and Czyńska (2015), Wrózynski et al (2016) or Rød and van der Meer (2009). All these studies focus on visibility analysis and skyline protection with respect towards specific buildings (structures) or panoramas which usually require data on regional scale.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Studies were visibility through linear vegetation are considered, e.g. our case study or study of Wrózynski et al (2016), would benefit from this approach as it is likely that the linear vegetation would offer some vistas that we are unable to model with current 2.5D surface model. Unfortunately, extended viewsheds were not yet used in visibility analyses calculated on voxel surface model.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Utilization of Large Scale Surface Models For Detailed Visibility Analyses

Caha

Kačmařík

2017

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

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This article demonstrates utilization of large scale surface models with small spatial resolution and high accuracy, acquired from Unmanned Aerial Vehicle scanning, for visibility analyses. The importance of large scale data for visibility analyses on the local scale, where the detail of the surface model is the most defining factor, is described. The focus is not only the classic Boolean visibility, that is usually determined within GIS, but also on so called extended viewsheds that aims to provide more information about visibility. The case study with examples of visibility analyses was performed on river Opava, near the Ostrava city (Czech Republic). The multiple Boolean viewshed analysis and global horizon viewshed were calculated to determine most prominent features and visibility barriers of the surface. Besides that, the extended viewshed showing angle difference above the local horizon, which describes angular height of the target area above the barrier, is shown. The case study proved that large scale models are appropriate data source for visibility analyses on local level. The discussion summarizes possible future applications and further development directions of visibility analyses.

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“…It supports the entirety of the 3D pipeline-modeling, rigging, animation, simulation, rendering, compositing and motion tracking, video editing, and game creation. The great versatility of the program allows for performing a 3D visual impact analysis [39].…”

Section: Classified Digital Surface Model (Cdsm) Developmentmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

Self Cite

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The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.

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The application of GIS and 3D graphic software to visual impact assessment of wind turbines

Cited by 52 publications

References 32 publications

Generating 3D city models without elevation data

Generating 3D city models without elevation data

Utilization of Large Scale Surface Models For Detailed Visibility Analyses

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

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