This thesis explores the development and validation of a synthetic framework for oblique aerial image adjustment and object point detection, with the goal of improving photogrammetric workflows in complex urban environments. The research is motivated by the inherent challenges of oblique imagery, such as occlusion, perspective distortion, and variable visibility, which complicate traditional adjustment procedures. To address these issues, the study employs a novel approach by generating synthetic test cases that emulate real-world oblique aerial data, enabling controlled experiments and sensitivity analyses. Utilizing data from recent aerial campaigns over Rotterdam, including both nadir and oblique images, the research implements and evaluates various adjustment and feature detection algorithms, including Bundle, DISK, SIFT, and LightGlue. The synthetic framework allows systematic testing of key parameters and environmental conditions, such as occlusion and lighting variations, providing insights into the robustness and limitations of different methods. Although the results demonstrate promising potential for synthetic data to replicate key geometric and photogrammetric behaviors, challenges remain in achieving full photorealism and seamless transferability to real-world applications. The findings underscore the importance of synthetic data in advancing urban geospatial systems and support the early-stage design of aerial collection systems, with particular relevance for municipal maintenance, planning, and infrastructure management in the Netherlands. The study concludes with recommendations for future research directions, emphasizing the integration of more photorealistic synthetic imagery and improved synthetic-to-real transfer methods to enhance the accuracy and reliability of oblique aerial mapping workflows. Overall, this work contributes to the growing body of knowledge on synthetic data use in photogrammetry and opens pathways for more resilient and efficient urban mapping solutions.

High-resolution image mosaicking plays a critical role in geomatics and remote sensing
applications, allowing efficient visualization, measurement, and analysis of large-scale envi
ronments. Although existing commercial tools provide standard stitching capabilities, they
often lack mathematical transparency and real-time customization, limiting their utility in
research and professional analysis.
This thesis introduces a systematic approach to dynamic image stitching and visualization
within a C# environment. The method uses homography transformations to achieve ac
curate image alignment while integrating an optimal seam-finding algorithm to improve
visual coherence in overlapping regions. An exportable homography matrix supports co
ordinate traceability, enabling users to perform metric evaluations on stitched images. The
implementation focuses on creating a lightweight, interactive stitching prototype capable of
processing two to three aerial images with high geometric fidelity and run-time efficiency.
Experimental validation confirms that the system delivers precise stitching results and sup
ports visual exploration for measurement tasks. By combining mathematical clarity, dy
namic responsiveness, and user adaptability, this research contributes to a modular and
extensible foundation for image mosaicking in the context of geomatics, with practical rele
vance for aerial inspection, photogrammetry, and spatial data visualization