This thesis addresses the challenge of extracting wireframe models which consist of 3d line segments from point clouds of man-made urban linear objects, with a specific focus on power lines and pylons. Wireframe models are essential for various applications including 3D city modeling, infrastructure monitoring, and urban planning. However, the automatic extraction of accurate wireframes from sparse airborne LiDAR point clouds remains challenging due to the complexity of these structures. Current wireframe extraction research either require high-quality data for model fitting or depend on complex pre-processing steps, lacking generality. To address these challenges, this thesis proposes a comprehensive evaluation of multiple wireframe extraction approaches and introduces an energy minimization framework which aims to address the limitations of the existing algorithms.

This thesis investigates four different algorithms for wireframe extraction: 3D RANSAC, 3D-2D RANSAC, Region Growing, and Hough Transform to address their limitations. Additionally, an approach of energy minimization for Markov Random Field is proposed to explore the potential of energy minimization methods in wireframe model extraction. Each algorithm is evaluated using a dataset of power lines and pylons from the Netherlands, with manually extracted wireframes serving as ground truth.

Experimental results demonstrate that each algorithm exhibits distinct advantages and limitations. The 3D RANSAC algorithm struggles with cylinder radius estimation and overlooks significant portions of input data. The 3D-2D RANSAC approach reduces dependency on normal estimation but still faces challenges with fitting accuracy. Region Growing achieves lower overlooking rates but suffers from scattered distribution of extracted elements. Hough Transform performs well on simple structures without requiring normal information but becomes computationally expensive for complex cases. The proposed energy minimization method shows promising results in preserving structural integrity by processing dense input graphs, particularly for complex structures with internal components.

Common limitations across all approaches include difficulties in normal estimation from sparse point clouds, misalignment between extracted primitives and ground truth, and challenges in balancing completeness and accuracy. The research emphasizes the complexity of wireframe extraction from point clouds and provides insights for developing more robust methods that combine the strengths of different approaches while addressing their mutual limitations.

By providing shade for residents in urban areas, cool spaces have been shown to be essential for mitigating the effects of heat stress. In response, the Municipality of Amsterdam developed a map showing walking distances to these spaces. However, the map lacks key information on capacity, accessibility, and precise distance measurements. This project addresses these gaps by identifying quality indicators for cool places and mapping their locations and quality scores across Amsterdam. Additionally, it establishes methods for computing the shortest and shadiest pedestrian routes to these spaces, enabling efficient routing to and from any given location.

To address the research questions, the following procedures were conducted. First, shade maps of Amsterdam were created for each warm month using the Daily Shadow Pattern tool of the Urban Multi-scale Environmental Predictor (UMEP). Second, cool spaces were identified and evaluated based on accessibility, shading, usability, capacity, heat risk, and Physiological Equivalent Temperature (PET) indicators. Lastly, after obtaining and processing the pedestrian network from the Open Street Map database, shade weight was calculated for each street segment, and cool spaces were incorporated into the network, allowing users to generate datasets of the shortest and shadiest distances to cool spaces, and an algorithm that performs four different routing options: the shortest, the shadiest, and two combinations of the shortest and shadiest paths with different weighting ratios either between two locations or from a starting point to its nearest cool space.

The project produced datasets which provide insights into Amsterdam’s cool spaces, their quality, and the shadiest and shortest routes to these locations. Additionally, the code to make these datasets has been made available on GitHub.