Linh Truong-Hong

Flying Voxel Method with Delaunay Triangulation Criterion for Façade/Feature Detection for Computation

AbstractA new algorithm is introduced to directly reconstruct geometric models of building facades from terrestrial laser scanning data without using either manual intervention or a third-party, computer-aided design (CAD) package. The algorithm detects building boundaries and features and converts the point cloud data into a solid model appropriate for computational modeling. The algorithm combines a voxel-based technique with a Delaunay triangulation–based criterion. In the first phase, the algorithm detects boundary points of the facade and its features from the raw data. Subsequently, the algorithm determines whether holes are actual openings or data deficits caused by occlusions and then fills unrealistic openings. The algorithm’s second phase creates a solid model using voxels in an octree representation. The algorithm was applied to the facades of three masonry buildings, successfully detected all openings, and correctly reconstructed the facade boundaries. Geometric validation of the models agains...

Point Cloud Data Conversion into Solid Models via Point-Based Voxelization

AbstractAutomated conversion of point cloud data from laser scanning into formats appropriate for structural engineering holds great promise for exploiting increasingly available aerially and terrestrially based pixelized data for a wide range of surveying-related applications from environmental modeling to disaster management. This paper introduces a point-based voxelization method to automatically transform point cloud data into solid models for computational modeling. The fundamental viability of the technique is visually demonstrated for both aerial and terrestrial data. For aerial and terrestrial data, this was achieved in less than 30 s for data sets up to 650,000 points. In all cases, the solid models converged without any user intervention when processed in a commercial finite-element method program.

Quantitative evaluation strategies for urban 3D model generation from remote sensing data

Over the last decade, several automatic approaches have been proposed to reconstruct 3D building models from aerial laser scanning (ALS) data. Typically, they have been benchmarked with data sets having densities of less than 25 points/m2. However, these test data sets lack significant geometric points on vertical surfaces. With recent sensor improvements in airborne laser scanners and changes in flight path planning, the quality and density of ALS data have improved significantly. The paper presents quantitative evaluation strategies for building extraction and reconstruction when using dense data sets. The evaluation strategies measure not only the capacity of a method to detect and reconstruct individual buildings but also the quality of the reconstructed building models in terms of shape similarity and positional accuracy. The paper presents the evaluation strategies for 3D building detection and building model reconstruction based on dense ALS data to benchmark the results in terms of quantifying the quality of the models, with respect to geometrical accuracy and the desired level of detail.Display Omitted High density aerial laser scanning data, approximate 225 points/m2.Building detection and building reconstruction from point clouds of urban building.Evaluation strategies for 3D building detection and building model reconstruction.Evaluation examining identical location, shape similarity and positional accuracy.Proposed method for generating building outlines.

Validating Computational Models from Laser Scanning Data for Historic Facades

Increasingly, remote sensing is being used as the basis for computational models. With new approaches rapidly emerging, questions arise as to how to validate and assess the resulting models, as they tend to include at least some level of geometric inexactitude. This paper proposes a set of parameters and procedures for evaluating the usefulness of computational models for structural analysis of historic facades subjected to adjacent construction work. To test the usability of such an approach, three brick buildings were scanned with a terrestrial laser scanner. The data were processed with a recently proposed set of algorithms, and the reliability of the resulting solid models was evaluated by comparing finite-element results from auto-generated solid models versus those based on measured drawings. The proposed validation process considers overall response, as well as local behavior. The results show the importance of using both conventional values and project specific parameters.

New Advances in Automated Urban Modelling from Airborne Laser Scanning Data

Traditionally, urban models in many applications such as urban planning, disaster management, and computer games only require visual accuracy. However, more recently, updating urban infrastructure combined with the rise of mega-cities (i.e. those with populations over ten million) has motivated researchers and users to utilize cityscale models for engineering purposes (e.g. tracking pollution monitoring, optimizing solar panel placement), which necessitate high geometric accuracy. Currently, a major bottleneck lies in the cost of generating accurate, geo-spatially referenced models. This paper presents the evolution of some of the efforts to automatically produce such models. Specifically, recent advances in airborne laser scanning can rapidly acquire accurate, spatial data for large geographic areas in hours, but due to the size of the data sets, coupled with difficulties of capturing and portraying complex structures, many post-processing issues have only recently been addressed to a level sufficient to begin to facilitate automation, especially of building surface reconstruction. Automation is a critical step for further processing and utilization of airborne laser scanned data for engineering-based, urban modeling. This paper presents recent development of the methods for building detection and extraction, with an emphasis on patents and other contributions related to automated processing of airborne laser scanning data.

Aerial laser scanning and imagery data fusion for road detection in city scale

This paper presents a workflow including a novel algorithm for road detection from dense LiDAR fused with high-resolution aerial imagery data. Using a supervised machine learning approach point clouds are firstly classified into one of three groups: building, ground, or unassigned. Ground points are further processed by a novel algorithm to extract a road network. The algorithm exploits the high variance of slope and height of the point data in the direction orthogonal to the road boundaries. Applying the proposed approach on a 40 million point dataset successfully extracted a complex road network with an F-measure of 76.9%.

Toward a new approach for massive LiDAR data processing

Laser scanning (also known as Light Detection And Ranging) has been widely applied in various application. As part of that, aerial laser scanning (ALS) has been used to collect topographic data points for a large area, which triggers to million points to be acquired. Furthermore, today, with integrating full wareform (FWF) technology during ALS data acquisition, all return information of laser pulse is stored. Thus, ALS data are to be massive and complexity since the FWF of each laser pulse can be stored up to 256 samples and density of ALS data is also increasing significantly. Processing LiDAR data demands heavy operations and the traditional approaches require significant hardware and running time. On the other hand, researchers have recently proposed parallel approaches for analysing LiDAR data. These approaches are normally based on parallel architecture of target systems such as multi-core processors, GPU, etc. However, there is still missing efficient approaches/tools supporting the analysis of LiDAR data due to the lack of a deep study on both library tools and algorithms used in processing this data. In this paper, we present a comparative study of software libraries and new algorithms to optimise the processing of LiDAR data. We also propose new method to improve this process with experiments on large LiDAR data. Finally, we discuss on a parallel solution of our approach where we integrate parallel computing in processing LiDAR data.

Documentation of Bridges by Terrestrial Laser Scanner

Bridge structures are subjected to deterioration due to excessive usage, overloading, and aging material. For the last two decades, a significant amount research has been developed for collecting data for structural health monitoring. Yet, visual investigation with an on-site inspector remains the predominant method. This is true despite the highly subjective and time consuming aspects of this approach. Alternatively, terrestrial laser scanning can acquire surface details of structures quickly and accurately and is, thus, an emerging means to overcome the shortcomings of direct visual inspection. This paper presents a procedure for data collection for bridge inspection documentation and proposes a “cell-based method” for determination of structure deterioration (involving vertical deformation and lateral distortion), as well as surface loss due to corrosion. The Guinness Bridge built in 1880s located in Dublin council, Ireland is selected as a case study to illustrate the efficacy of the proposed method.

A Semi-Automatic Member Detection for Metal Bridges

Terrestrial laser scanners (TLSs) are prominent non-contact instruments for acquiring highly detailed geometries of bridge components in only minutes. A TLS can be a strategic instrument for data collection for bridge inspection and documentation, because it can reduce significantly required field time and auxiliary equipment. To deploy a TLS in this field, a semi-automatic method for post-processing a point cloud for documentation of a historic metal bridge is proposed. In this work, generating 3D model of existing structural members and identifying connection characteristics are mainly of interest. The Guinness Bridge built in 1880s in Dublin, Ireland is presented as a case study for the proposed semi-automatic workflow.

Equipment Considerations for Terrestrial Laser Scanning for Civil Engineering in Urban Areas

When renting or purchasing a terrestrial laser scanner, consideration must be given to a variety of factors including ease of use, accuracy, speed and cost. The following paper considers these aspects with respect to civil engineering applications in urban areas. One particular concern relates to the logistics of being in the field in terms of the equipment quality, the required space, power supply needs, and time needed for operation. Other factors relate to data acquisition, quality, and processing. To illustrate these issues, two terrestrial laser scanners (one from 2003 and one from 2013) were used to acquire a point cloud of three, masonry building facades in a dense urban setting. Also, sample solid models as required for computational modelling in civil engineering, were generated from these scan data sets. The resulting investigation showed that despite a 10-year age difference in the units, there was no appreciable improvement in the scan angle accuracy, and data from both units was successfully employed to reconstruct building models for Original Research Article Truong-Hong et al; JSRR, Article no. JSRR.2014.15.003 2003 computation. However, the newer scanner was significantly faster in data acquisition and possessed other features that made it easier and more effective to deploy in urban areas, where space is limited and vehicular and pedestrian traffic can be problematic. The paper also provides a cost comparison of the two units showing that financial competitiveness depends upon the workload. This paper provides information for firms considering purchasing, renting, or subcontracting laser scanners for civil engineering projects in urban areas.