WK
W.H.J. Kahn
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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.
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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.
Student report
(2024)
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M.M. van Arnhem, Q. YANG, S.R.H.W. Tew, X. Zhao, W.H.J. Kahn, E. Verbree, Y.Y. Yu, Florent Poux
In recent years, the need for heritage preservation and reconstruction has become evident as many mature buildings face the risk of deterioration, damage or loss due to factors such as urban development, environmental weathering as well as outdated infrastructure. This urgency has created surges of significant interest to find sustainable methods of heritage preservation. The rise of emerging digital technologies has introduced a multitude of innovative methods for storing, analysing, and showcasing building data.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels. ...
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels. ...
In recent years, the need for heritage preservation and reconstruction has become evident as many mature buildings face the risk of deterioration, damage or loss due to factors such as urban development, environmental weathering as well as outdated infrastructure. This urgency has created surges of significant interest to find sustainable methods of heritage preservation. The rise of emerging digital technologies has introduced a multitude of innovative methods for storing, analysing, and showcasing building data.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels.
Technologies such as 3D LiDAR scanning, and Building Information Modelling enable detailed documentation and virtual exploration of heritage sites, while digital databases and archives facilitate the easy access and use of historical records. This project will attempt
to address a new method of heritage preservation by using Gaussian Splatting in conjunction with segmentation methods to create a visually accurate model while also incorporating semantic labels.