Autocorrelation Statistics-Based Algorithms for Automatic Ground and Non-Ground Classification of Lidar Data

Shirowzhan, Sara; Lim, Samsung

doi:10.22260/isarc2014/0121

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…Digital progress monitoring and job-site controlling tools: laser scanner [141], lidar [142][143][144][145][146], Internet of Things sensors, and photography camera [147]. • Digital contract management tools: intelligent or smart contracts.…”

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confidence: 99%

Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

et al. 2019

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Delay is one of the main challenges of construction projects, and there is still much to overcome in order to reach near zero delay in all construction projects. This project aims to conduct a systematic critical review including a bibliography analysis on delay literature in construction. The main questions consider what has been learnt from a decade investigating delay causes and effects in the construction literature and what factors have been missed in the literature. This paper also presents a new and challenging question regarding how digital tools and associated technologies may prevent any delay in construction projects, which can change the research direction from delay investigations to identifying prevention factors. The paper identifies the delay dataset, including 493 papers investigating delay in construction, and establishes a specific dataset of papers focusing on delay effects and causes (DEC), including 94 selected papers covering different factors examined in over 29 countries such as Iran, India, Turkey, Bangladesh, Saudi Arabia, the United Arab Emirates (UAE), Cambodia, Oman, Malaysia, Taiwan, China, Vietnam, the US, the UK, and Egypt. In addition, the paper identifies 30 critical factors with the frequency of occurrences over three times in the DEC dataset and computes their medians of ranking. This paper also discusses digital tools and methods that can be used for delay analysis and preventions, including MS Project, Oracle Primavera P6, and Open Plan by Deltek. The paper discusses the project schedule delay analysis from project management methodology perspectives. It also discusses the current method's limitations and future directions, which are based on the identification of the deficiency areas. In total, four overlooked factors are identified and suggested, including faulty data analysis, unmatched structure of the research questionnaires with new knowledge and standards [e.g., Project Management Body of Knowledge (PMBOK)], overlooked effects of digital technologies [e.g., Digital twin, Navisworks, Building Information Model (BIM), Geographic Information System (GIS), and Integrated Project Delivery (IPD)], and ignored job-site technologies. In addition, the paper presents the DEC model for future studies, including four main key factors. These factors are resources (e.g., project budgets, labour, material, equipment, and digital tool), project context, stakeholders performance (e.g., owner/client, consultant/designer, contractor, vendor/supplier), and external factors (e.g., ground condition, site location, regulation, natural disaster), which may significantly affect delay prevention and should be concurrently considered in the future delay investigations, since they may be required for designing an effective mitigation strategy when these proof points are identified. This would significantly help to utilise digital systems to prevent time overruns in different construction contexts. Several cases of 24 projects were delayed more than 100% of contract duration [44]. Indonesia 38% o...

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Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

et al. 2019

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“…The need for knowledge discovery and spatial data mining 'to extract unknown and unexpected information from spatial data sets' was suggested by Mennis and Guo [21]. Two popular SDM methods being used in geographic information systems (GIS) and remote sensing are spatial autocorrelation statistics [14,21,22] and nonparametric density estimation [23]. However, the potential of these SDM methods to explore urban height patterns using airborne lidar data has yet to be actively investigated.…”

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confidence: 99%

“…However, the potential of these SDM methods to explore urban height patterns using airborne lidar data has yet to be actively investigated. While a spatial autocorrelation statistic known as local Moran's I (LMI) is used to find the distribution pattern of building heights, the elevations of buildings were aggregated into large-sized cells using the mean elevation value of the included buildings [3,22,24].…”

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Introductory Chapter: Infrastructure Management, Construction, Structure and Industry 4.0

Sepasgozar¹,

Shirowzhan²,

Tahmasebinia³

2020

Infrastructure Management and Construction

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“…In a preliminary study of the autocorrelation-based algorithms for ground classification [90], omission and commission errors were calculated for validation of the results. We investigated the effect of window size with the same shape on the level of error.…”

Section: Digital Elevation Model (Dem) Generation In Slant Areasmentioning

confidence: 99%

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Shirowzhan

Sepasgozar

2019

IJGI

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Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm.

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Autocorrelation Statistics-Based Algorithms for Automatic Ground and Non-Ground Classification of Lidar Data

Cited by 7 publications

References 17 publications

Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

Introductory Chapter: Infrastructure Management, Construction, Structure and Industry 4.0

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

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