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Recent advances in high resolution, aerial LiDAR data collection can facilitate a more thorough understanding of three dimensional (3D) urban space across a range of viewpoints from the God’s eye view to the ground level. Using an extremely high resolution aerial LiDAR dataset collected over a 1.5km2 area of central Dublin, Ireland as a case study, this work pursues new vertical and volumetric understandings of the controversial Spire of Dublin. Viewing this structure in a fully elaborated, 3D environment with the capacity to experience the space from a range of perspectives enables a clearer understanding of the monument’s relative proportion to the space of the built environment both in terms of verticality and volume. Arguably, this in turn provides insight into relationships of power, modernity, tradition, and enclosure that inform a richer understanding of the arguments of both supporters and detractors of this piece of modern, public sculpture. This essay concludes with a suggestion of potential future work in high-resolution, aerial LiDAR collection to aid in developing resources in urban studies more broadly.

In this paper, a semi-analytical approach is proposed for free vibration analysis of a multi-span, orthotropic bridge deck with rubber bearings. This allows more realistic modeling of vibration transmission from a bridge’s deck to its supports. The approach is based on modal superposition incorporating intermodal coupling. The bridge deck was modeled as a continuous, multi-span, orthotropic rectangular plate with equivalent rigidities. The rubber bearings were inserted between the girders and rigid supports to absorb traffic-induced vibrations. The rubber bearing was modeled by linear elastic, vertical supports as very flexible in rotation and highly rigid in the vertical direction. The method’s efficacy was validated against two numerical examples. The absolute error was less than 10%.

Background: Conceptual models are an essential phase in software design, but they can create confusion and reduced performance for students in Database Design courses. Objective: A novel Relational Data Model Validation Tool (MVTool) was developed and tested to determine (1) if students who use MVTool perform better than those who do not, and (2) if design skills improve after using MVTool. Method: After a pre-test of database design skills, 68 students were divided into matched-pair control and experimental groups. All completed a database design task, with the experimental group having access to MVTool and the control group having no access to the tool. Findings: Notable improvements in specific design skills could be consistently detected in students after the introduction of the tool. Implications: Validation tools such as MVTool may help students to understand modeling languages and conventions used in database design, thereby improving their skill development and course outcomes.

Road networks are essential elements of a community's infrastructure and need regular inspection. Present practice requires traffic interruptions and safety risks for inspectors. The road detection system based on vehicle-mounted lasers is also quite mature, offering advantages such as high-precision defect detection, high automation, and fast detection speed. However, it does have drawbacks such as high equipment procurement and maintenance costs, limited flexibility, and insufficient coverage range. Therefore, this paper proposes a low-cost unmanned aerial vehicle (UAV)-based alternative using imagery for automatic road pavement inspection focusing on pothole detection and classification. A slicing-based method, entitled the Pavement Pothole Detection Algorithm, is applied to the imagery after it is converted into a three-dimensional point cloud. When compared with manually extracted results, the proposed UAV-structure-from-motion (SfM) method and the associated algorithm achieved 0.01 m level accuracy for pothole depth detection and maximum errors of 0.0053 m3 in volume evaluation for cases studies of both a road and a bridge deck. [ABSTRACT FROM AUTHOR]

A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%. [ABSTRACT FROM AUTHOR]