Sara Shirowzhan

Utilisation of a New Terrestrial Scanner for Reconstruction of As-Built Models: A Comparative Study

Recent studies show a great potential of improving construction productivity through using real-time applications to measure change orders and other objects to create as-built. As an enhancement to this promising concept, this paper presents the process of implementation of an efficient framework for as-built Building Information Modelling (BIM) using a new Scan Station that enables contractors to acquire accurate data and create and update the asbuilt models. A novel framework designed and assisted in the conversion of the raw 3D point cloud data from a laser scanner positioned at multiple locations of a facility into a compact and semantically rich model. The result of the study was compared with two other as-built models using mobile and terrestrial lidar scanners to understand the capability of each for building information modelling. It was found that the framework using the new Scan Station has a great potential to collect accurate data that can be transferred to BIM for creating as-built models. The results show that the accuracy of fine objects’ dimensions varies from -2% to +2%. The comparison of the test results with our previous experiments using different scanners shows that every scanner has its own advantage for each job in construction.

Enhanced Autocorrelation-Based Algorithms for Filtering Airborne Lidar Data over Urban Areas

AbstractMany existing algorithms for light detection and ranging (lidar) data classification are known to perform reliably; however, the automation of the classification of complex urban scenes is still a challenging problem. In this paper, two classification algorithms based on spatial autocorrelation statistics, such as the Local Moran’s I and the Getis-Ord Gi*, are proposed. These autocorrelation statistics are computed over sample urban areas, including complex terrain with diverse building characteristics. The proposed autocorrelation-based algorithms are applied to airborne lidar point clouds over the complex urban areas to generate highly accurate digital elevation models (DEMs) and classify the lidar points as ground and nonground points by using the DEMs. It is also demonstrated that the minimum-based rasterization and slope-based filtering can be integrated to effectively remove outliers from the DEMs. The test results showed that the autocorrelation-based algorithms produce high-level assessmen...

Implementation of Rapid As-built Building Information Modeling Using Mobile LiDAR

The need for development of reliable and efficient real-time data acquisition systems has recently attracted a great deal of attention in the construction industry, basically due to the demands for highly frequent updates in most visualization, optimization and coordination-related applications. The predominant data that has been used in the construction industry so far is rather less accurate. Moreover, the conventional methods of data acquisition are based on fieldwork that is timeconsuming, expensive and labour-intensive. Accuracy of original data and efficiency of data acquisition could be enhanced using new lidar technologies. Lidar is the advanced remote sensing technology that is able to provide 3D data with centimetre to millimetre level accuracy effectively and efficiently. However, the implementation of 3D data for accurate as-built creation is still challenging especially for openings and fine details of the construction objects in an indoor environment. This paper presents a framework for rapid as-built modelling using 3D point cloud data captured by a handheld lidar. The procedure involves five key stages from data capturing to create a final model. This paper reports the implementation of the framework using the state-of-the-art mobile lidar to analyse fine details of a sample building. Lidar data of a sample building in an indoor environment is captured using a mobile laser scanner and is analysed after registration and segmentation processes. The reconstructed model using the as-built data is compared with the existing 2D AutoCAD plans of the sample building and the traditional measurements in order to verify the accuracy of the proposed method. The results of this on-going study confirm that the proposed model development technique can serve as a reliable tool for accurate development of rapid as-built building models (rABM). The accuracy ranges from 5 to 30 mm, depending on the object size and position. The proposed algorithm was shown to be highly efficient in identifying the main visible components in the buildings. INTRODUCTION New technologies such as 3D laser scanners and building information modelling (BIM) offer great possibilities in the construction engineering area (Love et al. 2014; Porwal and Hewage 2013; Volk et al. 2014). Since the new technologies 209 Construction Research Congress 2014 ©ASCE 2014

Development of Urban Sustainability Index Using 3-D Spatial Metrics

Advanced spatial technologies such as photogrammetry and lidar have improved the quality of spatial information and enable data processing for more accurate estimation of urban environment parameters. This study aims to develop a quantification method for urban sustainability indexes by using spatial metrics such as compactness, complexity and density. Although building height information is an important element of urban morphology, it has been neglected in previous studies. Hence, height information obtained by lidar is incorporated into the spatial metrics in this study. The spatial metrics are applied to four study cases. We have examined the metrics and concluded that the developed metrics can quantify the sustainable urban form concept more effectively. The main finding of this study confirms that the 3-dimensional spatial metrics differentiate the complexity of urban areas significantly. Another significance of this study is the high capability of spatial metrics for the quantification of sustainable urban forms in terms of complexity, compactness and density. The developed indexes can be used for the determination of the spatio-temporal changes of sustainable urban forms or the comparison of the cities in terms of a sustainable urban form using remotely sensed data.

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

Classification of lidar data to ground and nonground points is important for accurate topography mapping and reliable estimation of slope, volume and buildings’ geometry over urban areas. Manual or semi-automatic classification provides relatively good results, however, automatic classification in complex areas with diverse object sizes is still challenging. This research aims to propose two novel algorithms based on Getis-Ord Gi* (or Gi* for short) and Local Moran’s I (LMI) statistics to classify a lidar point cloud into a set of points representing ground and another set of points reflected from non-ground e.g. buildings and vegetation. The Two statistics, Gi* and LMI, have been widely used in cluster analysis to identify clustered features of high z-scores and low z-scores. Based on the two statistics, we proposed two classification algorithms that allow varying window sizes e.g. 100 m, 150 m and 200 m, and applied the algorithms to the lidar data in order to obtain optimal classification results. The results show that the Gi*-based algorithm decreases omission errors but increases commission errors when compared to the LMI-based approach. Overall the 100-m window size outperforms than the other window sizes in terms of feature extraction in slant areas, whereas the 150-m window size provides slightly better results in a complex scene of high-rise buildings and dense vegetation, and the 200-m w indow size is more efficient if large buildings are present in the study area. This feasibility study indicates that autocorrelation statistics such as Gi* and LMI can be effectively used to classify a lidar point cloud.

Extraction of Polygon Footprints from Lidar Data in Urban Environment

Accurate extraction of building footprints is important for the calculation of sustainable urban form indexes. In this study automatic extraction methods have been investigated for the building reconstruction. It is found that buildings with closed polygon footprints are difficult to obtain the desired accuracy. In this paper, a semi-automatic approach is proposed for the extraction of closed polygon footprints from lidar data. Two types of closed polygons have been investigated, where one type is a simple closed polygon and the other is a complex combination of multiple polygons. From the reference data that is surveyed with a total station, the building footprints have been extracted. The extracted footprints from lidar data have been compared with the surveyed footprints using a confusion matrix and Kappa Index. It is concluded that our approach is reliable because the closed polygon footprints can be extracted with a high Kappa Index. This indicates that the proposed procedure is suitable to be utilized for cadastral mapping of remote built up areas.

Three Dimensional Spatial Metrics for Compactness Assessment of Urban Forms

Developing measurement tools to assess urban sustainability is one of the new streams in built environment; however, automated methods of urban form assessment are technically difficult. While planar characteristics of urban forms have been studied traditionally using the spatial metrics from remote sensing data such as Landsat images, height information of urban environments is now playing an important role in urban energy exchange (e.g. solar energy gains), urban air circulation (which is affected by interactions between terrain and buildings) and urban microclimate. As incorporating the planar metrics and the height information has been rarely attempted in the sustainable urban form studies, we propose an automatic technique for the quantification of 3-dimensional urban compactness which is o ne of the main factors influencing sustainability. In this paper, we aim to utilise one of autocorrelation statistics known as Moran’s for the assessment of urban form compactness, considering both layout and elevation attributes. Additionally, Getis-Ord statistic is also used for further investigation on concentration of low or high urban features.