This study explores the application of advanced photogrammetry techniques to enhance the accuracy of Digital Elevation Models (DEMs) derived from satellite imagery. It focuses on refining and integrating the photogrammetry pipeline to accommodate diverse satellite sources effectively. With recent advancements in photogrammetry and the expanding accessibility of satellite imagery, there is a growing opportunity for precise earth surface modeling. This research adapts a traditional photogrammetry pipeline to include state-of-the-art computer vision algorithms, specifically the DISK algorithm for feature detection and LightGlue for feature matching. These enhancements are complemented by tailored adjustments to camera models and projection matrices to suit the unique characteristics of satellite data.

The methodology emphasizes the modification of existing pipelines to optimize the handling of satellite images, incorporating sophisticated feature detection and matching technologies. The performance of these adaptations is rigorously evaluated through extensive analysis using satellite imagery across varied resolutions and environmental conditions. Results from the study indicate marked improvements in the fidelity and accuracy of the generated DEMs, which are substantiated by validation against high-resolution LiDAR ground truth data.

The refined pipeline effectively manages multi-source satellite images and produces terrain models of significantly higher quality, vital for robust geospatial analysis. This work not only bridges the gap between remote sensing and computer vision but also lays the groundwork for future research aimed at improving DEM generation from satellite imagery. This study proposes potential transformative practices in geospatial analysis and supports continued progress in fields such as environmental monitoring, urban planning, and disaster management.

The railway industry is constantly growing to meet the demand of society for stable, accessible and sustainable transportation. With this growth, the need for the railway to be reliable increases, requiring frequent surveying and maintenance. Fugro's RILA (Rail Infrastructure aLignment Acquisition) mobile mapping system contributes by making the surveying more accessible to the relevant railway network stakeholders. However, the system has its limitations in environments where the GNSS (Global Navigation Satelite System) signal is occluded, such as tunnels and underground stations. The geo-data collected by RILA in those areas is poorly georeferenced due to the poorly tracked trajectory of the system, which introduces spatial data misalignment up to a meter or more. The current methods to fix data misalignment rely on manual data corrections, which is not cost-effective, or on automatic solutions, which have limited applicability.

Thus, the aim of this research is to develop an improved trajectory optimization method, thereby ensuring accurate geo-referencing and alignment of the survey data. This thesis proposes a newly developed methodology to achieve this aim: features are extracted from point cloud surveys, matched and utilized by g2o optimizer and GNSS processing software to optimize the trajectory. The development is described and results are evaluated on two different scales - locally, within a point cloud tile and globally, within a sequence of tiles. It is done by using Glasgow's underground railway network as a test case.

Results from the implementation demonstrate significant improvements in trajectory accuracy - a misalignment of point cloud data was reduced from a 1.5 m to a cm level within an optimization time frame that took approximately 10 hours. This improvement in accuracy was present under different complex environments using both the local and global versions of the algorithm. However, the area near the railway tunnel entrance saw a limited benefit from the implementation of the proposed algorithm.

In conclusion, the developed trajectory optimization algorithm optimizes the trajectory and improves the alignment of the survey data. Moreover, the method outperforms the currently employed solutions by being automatic and applicable in different environments. However, further research is required to optimize the algorithm itself (accuracy and computationally speed of the algorithm) and to more accurately define its limitations in terms of the surveyed environments.

Photogrammetry is a well-established technique that has a significant impact on the use of images for mapping purposes. Employing feature extraction, matching, and the bundle adjustment, a large number of images can be automatically processed. Ingenieursbureau Geodelta developed an application for measurements in processed (oblique) aerial images. However, the quality of these measurements and the added value of oblique images on the adjustment's quality were unclear. This will be assessed by means of the theoretical standard deviation resulting from the bundle adjustment. Upon estimating this quality, an adapted RANSAC method is proposed that incorporates this quality as weights within its algorithm to extract geometric features. The objective is to evaluate whether this enhances the RANSAC results and could be applied to 3D reconstructions.

The results indicate three key factors that influence the theoretical standard deviation: high tie point availability, larger observation angles, and image viewing direction. With this, the theoretical standard deviation for tie points in both Nadir and Oblique image sets separately approximates 3 centimeters in the horizontal direction and about 10 in the height direction. Combining the two sets enhances the results by nearly a factor of three in all directions because the Nadir images connect the Oblique images, combining the strong characteristics of both sets. This demonstrates the value of both Nadir and Oblique imagery. For image exteriors, the improvement is even more pronounced, yielding an improvement factor of 4 or 5. However, propagating this quality metric through a dense matching algorithm in an adapted, weighted RANSAC algorithm does not show significant improvements in the number of planes found or the percentage of points classified as inliers of those planes. Furthermore, the RANSAC method does not converge to a better result in fewer iterations using the proposed method.

There are multiple types of objects distributed along the rail trackside to support the operation of railroads. Damage to those objects will result in delay and cancellation of trains. Therefore, the maintenance of the objects is important to ensure the safety of railway operations, and the key to maintenance is to know the accurate position of the objects. RILA - a railway mobile mapping system developed by Fugro - provides continuous monitoring of the rail environment without interrupting the daily operation of rail tracks. The RILA dataset contains different types of data including video frames, point clouds, and measurement trajectories from the Global Navigation Satellite System and Inertial Measurement Unit (GNSS-IMU). This enables a remote and automatic object positioning process based on object recognition in video frames and positioning using photogrammetric techniques.

Most of the rail trackside objects are parts of the railway electrification system and the railway signaling system. The available ground truth data contains more information about signaling objects, so this thesis mainly focuses on the positioning of signaling objects. Three typical components: signal lights, railway cabinets, and railway side markers are selected to make case studies and develop the object detection and localization pipeline for them.

There are two main issues that existed in former research: One is object recognition only gives a 2D pixel position for an object, but we want to know its 3D position in the world; the other is the contextual information between frames is ignored in object positioning. Based on that, this thesis developed a new workflow: firstly, this thesis proposes the use of an existing 3D object detection model, Single-Stage Monocular 3D Object Detection via Keypoint Estimation (SMOKE), to recognize objects of interest from the RILA video frames. This model can not only predict an object's 2D pixel position on the image, but also its 3D position with respect to the camera. This enables the localization of an object in each of the frames it is detected in, which provides redundancy to define its final position. Secondly, this thesis developed a pipeline to clean the detection output of SMOKE so that most false detections can be removed. In addition, this thesis developed a method to use the contextual information in image sequences to position objects: first, extract the sequences by classifying all the predictions in the detection output through the Euclidean distances, then for each sequence, analyze the varying trend of predicted positions to select reliable positions. The mean of the selected positions is the final improved position of that object. By making tests on a part of the railway near Ely, UK, with 2983 frames, the workflow detected and positioned all of the 4 signal lights, all of the 10 markers, and 13 of the 14 cabinets in the testing dataset. Most signal lights and cabinets can be positioned within 1 meter to the ground truth, and the mean positioning offset for the railway side markers can be controlled around 2 meters. Complex scenarios could be solved, when up to three cabinets were located close together.

The Dutch highway network contains more than 3.000 kilometers of roads. Along these roads are thousands of overhead objects such as viaducts and traffic sign gantries. It is essential to have recent and accurate data on the clearances under these objects. This data is important for maintenance and routing oversized transports. Obtaining clearance measurements can be time consuming, costly and in involves a lot of manual labor. The aim of this study is to develop a method that automatically estimates vertical and horizontal clearances of highway viaducts and gantries from Mobile Laser Scanning (MLS) point clouds. The proposed method takes a point cloud of an infrastructure object as input, and as output provides the user with a concise overview of the estimated horizontal and vertical clearances under the object.

A point cloud of a highway viaduct or gantry is segmented into different clusters relevant for determining the clearances. The discrete points in these clusters are then used to approximate their corresponding surfaces with B-splines. Subsequently the minimal clearances can be estimated. These clearances are estimated at certain pre-specified locations according to guidelines from the highway authority, Rijkswaterstaat. To validate the proposed method a case study is performed on two sections of Dutch highway containing a total of 20 viaducts and 50 gantries. For the viaducts and gantries along these highway sections there are clearance measurements available from third-party contractors. After processing the point clouds in the case study, the obtained clearance estimations are compared to the third-party measurements. This comparison gives a quantitative analysis of the estimated clearances and shows that the proposed method produces similar results to the third-party measurements. On average the proposed method overestimates the vertical clearances and underestimates the horizontal clearances. A sensitivity analysis is performed to confirm that the proposed method can produce consistent results. When performing clearance estimations on a different dataset containing point clouds with an up to 20 times higher point density, the estimation differences with the third-party measurements become even smaller.