Debra F. Laefer

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.

Three-dimensional spatial information systems : state of the art review

A spatial information system (SIS) is critical to the hosting, querying, and analyzing of spatial data sets. The increasing availability of three-dimensional (3D) data (e.g. from aerial and terrestrial laser scanning) and the desire to use such data in large geo-spatial platforms have been dual drivers in the evolution of integrated SISs. Within this context, recent patents demonstrate efforts to handle large data sets, especially complex point clouds. While the development of feature-rich geo-systems has been well documented, the implementation of support for 3D capabilities is only now being addressed. This paper documents the underlying technologies implemented for the support for 3D features in SISs. Examples include ESRIs ArcGIS geo-database with its support for two-and-a-half dimensions (2.5D) in its Digital Elevation Model (DEM) and Triangular Irregular Network (TIN), the more recent development of the Terrain feature class, and support for 3D objects and buildings with its multi-patch feature class. Recent patents and research advances aim to extract DEMs and TINs automatically from point cloud data. In this context, various data structuring innovations are presented including both commercial and open source alternatives.

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.

Predicting RC Frame Response to Excavation-Induced Settlement

In many tunneling and excavation projects, free-field vertical ground movements have been used to predict subsidence, and empirical limits have been employed to evaluate risk. Validity of such approaches is largely unknown given that ground movements are in fact not one-dimensional and that adjacent applied loads are known to have an impact. This paper employed analytical and large-scale experimental efforts to quantify these issues, in the case of excavation adjacent to a reinforced concrete frame with tieback anchors and a sheetpile wall in dry sand. With this flexible system, a disproportionate amount of the soil and building movements occurred prior to installation of the first tieback, even when conservative construction practices were applied. Furthermore, free-field data generated a trough as little as one-half the size of that recorded near the building frames. Empirically based relative gradient limits generally matched the extent and distribution of the damage, while the application of various structural limits did not fully identify local damage distribution but did generally reflect global response. The use of fully free-field data or a failure to include lateral soil displacements both underpredicted building displacements by as much as 50% for low-rise concrete frames without grade beams on sand.

Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS

A large proportion of todays digital datasets have a spatial component. The effective storage and management of which poses particular challenges, especially with light detection and ranging (LiDAR), where datasets of even small geographic areas may contain several hundred million points. While in the last decade 2.5-dimensional data were prevalent, true 3-dimensional data are increasingly commonplace via LiDAR. They have gained particular popularity for urban applications including generation of city-scale maps, baseline data disaster management, and utility planning. Additionally, LiDAR is commonly used for flood plane identification, coastal-erosion tracking, and forest biomass mapping. Despite growing data availability, current spatial information systems do not provide suitable full support for the datas true 3D nature. Consequently, one system is needed to store the data and another for its processing, thereby necessitating format transformations. The work presented herein aims at a more cost-effective way for managing 3D LiDAR data that allows for storage and manipulation within a single system by enabling a new index within existing spatial database management technology. Implementation of an octree index for 3D LiDAR data atop Oracle Spatial 11g is presented, along with an evaluation showing up to an eight-fold improvement compared to the native Oracle R-tree index.

Big-Data Approach for Three-Dimensional Building Extraction from Aerial Laser Scanning

AbstractThis paper proposes a big-data approach to automatically identify and extract buildings from a digital surface model created from aerial laser scanning data. The approach consists of two steps. The first step is a MapReduce process where neighboring points in a digital surface model are mapped into cubes. The second step uses a non-MapReduce algorithm first to remove trees and other obstructions and then to extract adjacent cubes. According to this approach, all adjacent cubes belong to the same object and an object is a set of adjacent cubes that belong to one or more adjacent buildings. Finally, an evaluation is presented for a section of Dublin, Ireland, to demonstrate the applicability of the approach, resulting in a 91% quality level for the extraction of 106 buildings over 1  km2, including buildings that have more than 10 adjacent components of different heights and complicated roof geometries. The proposed approach is notable not only for its big-data context but for its usage of vector data.

3D Reconstructions Using Unstabilized Video Footage from an Unmanned Aerial Vehicle

Structure from motion (SFM) is a methodology for automatically reconstructing three-dimensional (3D) models from a series of two-dimensional (2D) images when there is no a priori knowledge of the camera location and direction. Modern unmanned aerial vehicles (UAV) now provide a low-cost means of obtaining aerial video footage of a point of interest. Unfortunately, raw video lacks the required information for SFM software, as it does not record exchangeable image file (EXIF) information for the frames. In this work, a solution is presented to modify aerial video so that it can be used for photogrammetry. The paper then examines how the field of view effects the quality of the reconstruction. The input is unstabilized, and distorted video footage obtained from a low-cost UAV which is then combined with an open-source SFM system to reconstruct a 3D model. This approach creates a high quality reconstruction by reducing the amount of unknown variables, such as focal length and sensor size, while increasing the data density. The experiments conducted examine the optical field of view settings to provide sufficient overlap without sacrificing image quality or exacerbating distortion. The system costs less than e1000, and the results show the ability to reproduce 3D models that are of centimeter-level accuracy. For verification, the results were compared against millimeter-level accurate models derived from laser scanning.

Urban Point Cloud Mining Based on Density Clustering and MapReduce

AbstractThis paper proposes an approach to classify, localize, and extract automatically urban objects such as buildings and the ground surface from a digital surface model created from aerial lase...