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

Biljecki, Filip; Heuvelink, G.B.M.; Ledoux, Hugo; Stoter, J.E.

doi:10.1080/15230406.2017.1279986

Cited by 30 publications

(21 citation statements)

References 126 publications

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“…The method of constructing the CDSM for the analysis consisting of meshing and extruding the first return of the object is simple, fast, user-independent, and does not require any additional data but can generate geometry errors and inaccuracies especially in the case of buildings and tree objects. The QLA360 method can be directly applied for already existing 3D city models with different Level of Detail (LOD), which can improve analysis results due to the better buildings geometry representation [53,54], and virtual environments made with game engines [55]. The reconstruction of individual trees is possible but requires a very high density LiDAR collected using UAV platforms [56,57] or Terrestrial Laser Scanning [58,59] or manually modeling vegetation in 3D modeling software.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

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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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Section: Discussionmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

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“…[54]). In a broader perspective, it has been shown that for spatial analyses performed on entire cities the geometric accuracy of the building models has a larger impact on the result than the level of detail of the buildings [28].…”

Section: Discussionmentioning

confidence: 99%

“…There are also uncertainties in the geospatial data and situation plan; these uncertainties are both representation-induced (dependent on the LOD used) and acquisition-induced (cf. [28]). …”

Section: Analytical Rule Checkingmentioning

confidence: 99%

Automation of Building Permission by Integration of BIM and Geospatial Data

Olsson

Axelsson

Hooper

et al. 2018

IJGI

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Abstract:The building permission process is to a large extent an analogue process where much information is handled in paper format or as pdf files. With the ongoing digitalisation in society, there is a potential to automate this process by integrating Building Information Models (BIM) of planned buildings and geospatial data to check if a building conforms to the building permission regulations. In this study, an inventory of which regulations in the (Swedish) detailed development plans that can be automatically checked or supported by 3D visualisation was conducted. Then, two of these regulations, the building height and the building footprint area, were studied in detail to find to which extent they can be automatically checked by integration of BIM and geospatial data. In addition, a feasibility study of one visual criterion was conducted. One concern when automating the building permission process is the variability of content within the Industry Foundation Classes (IFC) data model. Variations in modelling methods and model content leads to differences in IFC models' content and structure; these differences complicate automated processes. To facilitate automated processes, requirements on the production of IFC models for building permission applications could be defined in the form of model view definitions or delivery specifications.

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“…Many users are not aware that there are a large number of options for modelling buildings as blocks based on their 2D polygon, while these options do influence the outcome of analyses for which the LoD1 models are used (Biljecki et al, 2018). In addition, usually little is known about the quality of the automatically reconstructed LoD1 models because no metadata is kept.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Height Lod1 Model of All Buildings in the Netherlands

Dukai

Ledoux

Stoter

2019

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

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<p><strong>Abstract.</strong> The 3D representation of buildings with roof shapes (also called LoD2) is popular in the 3D city modelling domain since it provides a realistic view of 3D city models. However, for many application block models of buildings are sufficient or even more suitable. These so called LoD1 models can be reconstructed relatively easily from building footprints and point clouds. But LoD1 representations for the same building can be rather different because of differences in height references used to reconstruct the block models and differences in underlying statistical calculation methods. Users are often not aware of these differences, while these differences may have an impact on the outcome of spatial analyses. To standardise possible variances of LoD1 models and let the users choose the best one for their application, we have developed a LoD1 reconstruction service that generates several heights per building (both for the ground surface and the extrusion height). The building models are generated for all ~10 million buildings in The Netherlands based on footprints of buildings and LiDAR point clouds. The 3D dataset is updated every month automatically. In addition, for each building quality parameters are calculated and made available. This article describes the development of the LoD1 building service and we report on the spatial analysis that we performed on the generated height values.</p>

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

Cited by 30 publications

References 126 publications

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Automation of Building Permission by Integration of BIM and Geospatial Data

A Multi-Height Lod1 Model of All Buildings in the Netherlands

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