Accuracy assessment of 3-dimensional LiDAR building extraction

Saraf, Noraain Mohamed; Hamid, Juazer Rizal Abdul; Halim, Maisarah Abdul; Rasam, Abdul Rauf Abdul; Lin, Saharuddin

doi:10.1109/cspa.2018.8368723

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…That study determined that QL2 data is limited to extracting buildings and trees in the streetscape. Since other studies linking LiDAR building classifications with building footprints show strong correlations (32,33), and since street trees are a significantly analyzed topic in transportation research, the study examined and found significant statistical differences between deriving 3D characteristics of trees against 2D LiDAR-derived polygons. QL2 data is limited in relation to usefulness for transportation-based feature extraction, however, as many more critical features other than building and trees exist in a typical streetscape environment.…”

Section: Literature Reviewmentioning

confidence: 98%

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

Golombek

Marshall

2020

Transportation Research Record

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This study investigates the feasibility of extracting streetscape features from high-density United States Geological Survey (USGS) quality level 1 (QL1) light detection and ranging (LiDAR) and quantifying the features into three-dimensional (3D) volumetric pixel (voxel) zones. As the USGS embarks on a national LiDAR database with the goal of collecting LiDAR across the continuous U.S.A., the USGS primarily requires QL2 or QL1 as a collection standard. The authors’ previous study thoroughly investigated the limits of extracting streetscape features with QL2 data, which was primarily limited to buildings and street trees. Recent studies published by other researchers that utilize advanced digital mapping techniques for streetscape measuring acknowledge that most features outside of buildings and street trees are too small to detect. QL1 data, however, is four times denser than QL2 data. This study divides streetscapes into Thiessen proximal polygons, sets voxel parameters, classifies QL1 LiDAR point cloud data, and computes quantitative statistics where classified point cloud data intersects voxels within the streetscape polygons. It demonstrates how most other common streetscape features are detectable in a standard urban QL1 dataset. In addition to street trees and buildings, one can also legitimately extract and statistically quantify walls, fences, landscape vegetation, light posts, traffic lights, power poles, power lines, street signs, and miscellaneous street furniture. Furthermore, as these features are processed into their appropriate voxel height zones, this study introduces a new methodology for obtaining comprehensive tabular descriptive statistics describing how streetscape features are truly represented in 3D.

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Section: Literature Reviewmentioning

confidence: 98%

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

Golombek

Marshall

2020

Transportation Research Record

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“…Today, different approaches and models of accuracy assessment of 3D building can be found [64][65][66]. The first group of approaches consists in comparing the created building model to the reference model, which is presented in the same form [67,68].…”

Section: Data Accuracy Assementmentioning

confidence: 99%

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Jovanović¹,

Milovanov²,

Ruskovski³

et al. 2020

IJGI

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The Smart Cities data and applications need to replicate, as faithfully as possible, the state of the city and to simulate possible alternative futures. In order to do this, the modelling of the city should cover all aspects of the city that are relevant to the problems that require smart solutions. In this context, 2D and 3D spatial data play a key role, in particular 3D city models. One of the methods for collecting data that can be used for developing such 3D city models is Light Detection and Ranging (LiDAR), a technology that has provided opportunities to generate large-scale 3D city models at relatively low cost. The collected data is further processed to obtain fully developed photorealistic virtual 3D city models. The goal of this research is to develop virtual 3D city model based on airborne LiDAR surveying and to analyze its applicability toward Smart Cities applications. It this paper, we present workflow that goes from data collection by LiDAR, through extract, transform, load (ETL) transformations and data processing to developing 3D virtual city model and finally discuss its future potential usage scenarios in various fields of application such as modern ICT-based urban planning and 3D cadaster. The results are presented on the case study of campus area of the University of Novi Sad.

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“…Other streetscape features such as signs, landmarks, power poles, and overhanging lights (to name a few) would initially have an automated LiDAR classification of ''Unclassified'' or ''Other'' and require manual classification. Studies linking LiDAR building classifications with actual building footprints show that the LiDAR processing community has made significant inroads with producing highly accurate building footprints from LiDAR (22,23).…”

Section: Overviewmentioning

confidence: 99%

Use of Aerial LiDAR in Measuring Streetscape and Street Trees

Golombek

Marshall

2019

Transportation Research Record

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This paper investigates the usefulness of 3D volumetric pixels (voxels) and the United States Geological Survey (USGS) Quality Level 2 (QL2) Light Detection and Ranging (LiDAR) data to measure features in streetscapes. As the USGS embarks on a national LiDAR database with the goal of covering the entire United States of America (U.S.) with QL2 data or better, this paper investigates uses of QL2 LiDAR for the 3D measuring of streetscapes. Tree mapping is a common use of QL2 LiDAR data, and street trees are among the most common features within urban streetscapes that transportation and urban designers assess. Traditional remote sensing techniques derive tree polygons from imagery, and traditional uses of LiDAR for tree canopy mapping is based on deriving a 2D canopy polygon with an attribute for elevation height. However, when breaking up streetscapes into 5 Ft elevation zones and calculating street–tree voxels at each elevation zone height, 3D characteristics of street trees become prevalent that completely differ from the common 2D LiDAR-derived street trees. Statistical tests in this paper display how different the 3D characteristics are from the 2D-derived LiDAR polygons, as this paper introduces a new methodology for measuring streetscape features in 3D, particularly street trees.

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Accuracy assessment of 3-dimensional LiDAR building extraction

Cited by 8 publications

References 8 publications

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Use of Aerial LiDAR in Measuring Streetscape and Street Trees

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