Now showing 1 - 7 of 7
  • Publication
    New possibilities for damage prediction from tunnel subsidence using aerial LiDAR data
    Computation modelling has not been fully exploited for predicting building damage due to tunnel-induced subsidence, because of the expense and time required to create computational meshes for the vast quantity of buildings that may be impacted along a tunnel’s route. A possible circumvention of such a resource commitment lies in the exploitation of remote sensing data in the form of aerial laser scans (also know as Light Detection and Ranging – LiDAR). This paper presents work accomplished to date in the creation of a pipeline to automate the conversion of aerial LiDAR point cloud data directly into Finite Element Method (FEM) meshes without the intermediary step of triangulation-based conversion or reliance on geometric primitives through a Computer Aided Design (CAD) program. The paper highlights recent advances in flight path planning, data processing, plane identification, wall segmentation, and data transformation.
  • Publication
    Combining an Angle Criterion with Voxelization and the Flying Voxel Method in Reconstructing Building Models from LiDAR Data
    Traditional documentation capabilities of laser scanning technology can be further exploited for urban modeling through the transformation of resulting point clouds into solid models compatible for computational analysis. This article introduces such a technique through the combination of an angle criterion and voxelization. As part of that, a k-nearest neighbor (kNN) searching algorithm is implemented using a predefined number of kNN points combined with a maximum radius of the neighborhood, something not previously implemented. From this sample, points are categorized as boundary or interior points based on an angle criterion. Façade features are determined based on underlying vertical and horizontal grid voxels of the feature boundaries by a grid clustering technique. The complete building model involving all full voxels is generated by employing the Flying Voxel method to relabel voxels that are inside openings or outside the façade as empty voxels. Experimental results on three different buildings, using four distinct sampling densities showed successful detection of all openings, reconstruction of all building façades, and automatic filling of all improper holes. The maximum nodal displacement divergence was 1.6% compared to manually generated meshes from measured drawings. This fully automated approach rivals processing times of other techniques with the distinct advantage of extracting more boundary points, especially in less dense data sets (<175 points/m2), which may enable its more rapid exploitation of aerial laser scanning data and ultimately preclude needing a priori knowledge.
      669Scopus© Citations 106
  • Publication
    Visualisation of urban airborne laser scanning data with occlusion images
    Airborne Laser Scanning (ALS) was introduced to provide rapid, high resolution scans of landforms for computational processing. More recently, ALS has been adapted for scanning urban areas. The greater complexity of urban scenes necessitates the development of novel methods to exploit urban ALS to best advantage. This paper presents occlusion images: a novel technique that exploits the geometric complexity of the urban environment to improve visualisation of small details for better feature recognition. The algorithm is based on an inversion of traditional occlusion techniques.
      289Scopus© Citations 13
  • Publication
    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 city-scale models for engineering purposes (e.g. tracking pollution monitoring, optimizing solar panel placement), which necessitates 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.
      568Scopus© Citations 8
  • Publication
    Flying Voxel Method with Delaunay Triangulation Criterion for Façade/Feature Detection for Computation
    (American Society of Civil Engineers, 2012-11) ; ; ;
    A new algorithm is introduced to directly reconstruct geometric models of building façades 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 façade 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 façades of three masonry buildings, successfully detected all openings, and correctly reconstructed the façade boundaries. Geometric validation of the models against measured drawings showed overall dimensions correct to 1.2%, most opening areas to 3%, and simulation results within 5% of those predicted by CAD-based models.
      2006Scopus© Citations 42
  • Publication
    Point Cloud Data Conversion into Solid Models via Point-Based Voxelization
    (American Society of Civil Engineers, 2013-05) ; ; ;
    Automated 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.
      2811Scopus© Citations 68
  • Publication
    Flight optimization algorithms for aerial LiDAR capture for urban infrastructure model generation
    (American Society of Civil Engineering (ASCE), 2009-11) ; ;
    Aerial Light Detection and Ranging (LiDAR) offers the potential to auto-generate detailed, three-dimensional (3D) models of the built environment in urban settings. Auto-generation is needed as manual generation is not economically feasible for large areas, and yet such models would offer distinct advantages for a wide range of applications from improved noise and pollution prediction to disaster mitigation modeling. Current technology and the dense geometry of urban environments are two major constraints in LiDAR scanning. This paper outlines the difficulties related to effective vertical surface data capture in an urban environment for the purpose of 3D visualization. Further, the traditional point data captured with LiDAR scans is unsuitable for visualization. Therefore, surface reconstruction algorithms must be applied to the data. These algorithms are largely dependent on the uniformity of the resolution in the point data. Principles for geometric optimization of data capture on vertical surfaces, thereby improving resolution uniformity, are presented.
      2152Scopus© Citations 43