Urban climate simulations increasingly rely on digital twins of cities, yet vegetation remains largely absent or oversimplified despite its strong influence on wind flow and heat exchange. Existing lidar-based tree studies mainly target forests or small plots, and no scalable method currently exists to reconstruct detailed urban trees for computational fluid dynamics (CFD) analysis.

This thesis presents a scalable, automated pipeline for reconstructing CFD-ready urban tree models from open-access airborne lidar. The workflow operates directly on unstructured point clouds and comprises three main components. The first introduces the High-Order Multi-Echo Density (HOMED) vegetation filter, a new approach for distinguishing vegetation from non-vegetation in airborne lidar point clouds. Combined with TreeSeparation, a cuboid-based tree instance segmentation algorithm, it produces clean per-tree clusters for subsequent analysis. The second explores taxonomy-based classification to assess structural separability between species. The third abstracts each segmented point cloud into CFD-suitable crown and trunk geometries and derives per-tree volumetric porosity values, ensuring watertightness, manifoldness, and controlled mesh complexity. Designed to be dataset-agnostic, the pipeline generalises to any comparable airborne laser scanning data.

The workflow was applied to several major Dutch cities—Amsterdam, Rotterdam, Utrecht, and Delft—processing hundreds of thousands of trees from raw point clouds to 3D models within practical runtimes (≈ 13 hours for Amsterdam on 16 CPU cores). Segmentation and reconstruction proved robust and consistent, yielding α-wrapped crowns, cylindrical trunk approximations, and physically meaningful porosity estimates. Taxonomic classification, however, was unreliable due to label noise, class imbalance, and limited structural separability in lidar-only data; supplementary optical features from RGB and infrared imagery were evaluated but proved unreliable for consistent integration.

The results demonstrate that the pipeline enables scalable reconstruction of CFD-ready tree models that preserve canopy structure and aerodynamic properties, allowing their explicit representation in urban digital twins and supporting more realistic urban climate simulations.

This project presents an indoor navigation system based on image matching, aiming to address the challenges of localization and navigation in indoor environments. The system utilizes Simultaneous Localization and Mapping (SLAM) technology to capture high-resolution images and point cloud data, combined with the VGG16 model from Convolutional Neural Networks (CNN) for image processing, feature extraction, and matching.

In our research, we conducted experiments at the Faculty of Architecture and the Built Environment of Delft University of Technology, using a SLAM scanner to obtain 360-degree panoramic images and point cloud data of the indoor environment. Through cube mapping projection, we converted the panoramic images into six planar views, selecting the front, right, and left views as positioning references. Additionally, we reconstructed the indoor environment structure and designed node networks for positioning and navigation.

The technical architecture of this system comprises three main components: VGG16-based image feature extraction, cosine similarity-based image matching, and DBSCAN algorithm for location clustering. Through this method, the system can achieve real-time localization results after image capture and provide users with optimal paths using the A* navigation algorithm.

Experimental results show that when using single image matching, the system's room localization accuracy reaches 74.65\%. When employing multiple image matching and DBSCAN clustering methods, the accuracy significantly improves. In our final evaluation involving 116 positions, the system successfully matched 111 of these positions to their correct rooms, achieving a localization accuracy of 95.69\%.

This research not only provides an innovative solution for indoor positioning and navigation but also points the way for future research, including support for multi-floor navigation, enhancing CNN model performance, and automating building processing. This technology has the potential for widespread application in complex indoor environments such as large buildings, conference centers, and university campuses, offering users accurate, real-time positioning and navigation services.