Current urban tree inventories rely heavily on time-consuming manual work and often fail to capture all trees. To effectively monitor the impact of urban trees on their environment and vice versa, an automated method for detecting and grouping trees based on their characteristics is crucial. This research aims to expand current urban tree inventories and cluster trees based on their characteristics. Existing inventories typically include species, age, and height, but trees of the same species and age can vary significantly due to environmental factors. This study focuses on a 500x600 meter area in Delft, encompassing 641 recorded trees from the municipal inventory. An automatic tree detection method using airborne LiDAR (AHN4) point cloud data combined with Random Forest classification was implemented, achieving an accuracy of 85-90%. Individual trees were identified using an existing tree segmentation algorithm, detecting 70% of recorded trees with a mean location difference of 0.71 meters and identifying an additional 460 trees, including those on private land. Despite promising results, limitations include the undetection of small trees (below 3 meters) and classification errors leading to missed detections, multiple identifications for single trees, and false positives. Geometric and reflectance features were extracted. Highresolution, 30cm, spectral images from the SuperView Neo satellites, acquired across three seasons, provided spectral features like tree color and NDVI. Overall resulting in a total of 50 features. This comprehensive inventory allows for clustering of individual trees based on geometric, reflectance, and spectral similarities using a K-means algorithm. The approach enhances urban tree inventories by incorporating new features from airborne Li- DAR and spectral images, such as tree height distribution, crown sphericity, and density. Seasonal changes from spectral images provide insights into tree behavior. These detailed features significantly improve clustering, effectively grouping similar trees together. The findings reveal that trees of the same species in seemingly similar environments exhibit significantly different characteristics. This research offers a method to enhance urban tree inventories and supports long-term studies to reveal how trees respond to urban development, climate change, and ecological dynamics. Correlating tree growth and health with specific locations and climatic conditions can aid in developing sustainable urban planning and conservation strategies.

LIght Detection And Ranging (LiDAR) imaging technology has advanced over the past two decades, being used for applications such as Digital Terrain Models (DTM) and Building Integration Modeling (BIM) integration. The resulting products, point clouds, serve diverse purposes, each demanding specific quality standards. Failing to meet the standards risks rendering the data ineffective or even unusable. Contractors, including Rijkswaterstaat, therefore, specify adherence to set requirements or standards. Current manual sampling for assessing the quality highlights the need for an automated tool.

This study proposes a workflow for automating quality validation of LiDAR infrastructure point clouds. The workflow assesses point cloud quality based on three primary components: coverage, relative accuracy, and absolute accuracy. The methodology includes:
• Point Cloud Density assessment: involves analyzing 2D horizontal cells and partial-3D spaces to ensure compliance with density requirements.
• Overlapping Regions Alignment: identifies and compares surfaces in overlapping areas of point clouds to determine relative accuracy.
• Benchmark alignment: extracts points corresponding to spherical targets, estimates the center coordinates, and evaluates adherence to absolute accuracy standards.

Quality assessments were conducted on static, mobile, and airborne point clouds. The static point cloud analysis revealed non-compliance with density requirements in 2D, with approximately half the points failing to meet standards. In 3D analysis, compliance was observed for 1 m2 horizontal cells, but individual 1-meter sections often fell short upon closer inspection. These findings highlight the need for tailored quality standards: detailed 3D analysis is crucial for complex environments like tunnels, while road environments can be effectively evaluated in 2D. Relative accuracy assessments for static and mobile datasets showed compliance with scanner specifications, with RMSE values meeting the specified requirements. Absolute accuracy assessments on static point clouds met requirements with minimal deviations in both XY and Z directions.

Recommendations include defining density requirements for different environments, establishing acceptance criteria, and defining allowable deviations for all requirements.

In 2003, the Hondsbossche and Pettemer Zeewering, located from Camperduin to Petten, was classified to not be within safety margins of the Dutch Coast. In order to protect the Dutch Coast from sea level rise, the Hondsbossche dunes were created in 2015. This project included the building of an artificial lagoon for recreational purposes. This lagoon is protected from the sea by three sand dunes. Without these dunes, the lagoon will not remain to exist. The aim of this study is to detect changes of the shape and the height of the dunes protecting the lagoon at Camperduin from the sea using annual lidar data.
JARKUS data is obtained from airborne laser scanning for the Dutch coastal areas which is done yearly between January and March. Eight datasets of annual lidar data which include the dunes are used for this study. The datasets consists of point clouds for the years 2016 to 2023.
To obtain information about the changes occurring, several methods are used. The workflow recommended is the C2M method for 3D changes. These changes can be clustered by K-means clustering. 2D changes can be observed using cross sections, contour lines and the volume changes.
The seaward side of the dunes decreases in height due to marine erosion such as storms. Around 4.5 meter erosion on some locations has been found due to large storms in 2022. The lagoon side of the dunes increases in height by aeolian transport. The rate of dune growth is found to be 0.5 meters per year.
The middle dune experiences more erosion than deposition and the volume decreases. The erosion is caused by less vegetation and human impacts. If the trend from before the storm in 2022 continues, the dune will shrink and disappear if no additional maintenance is done. The other two dunes do not experience big volume losses and are classified as stable.

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 city of Amsterdam faces the challenge of monitoring and assessing 200 kilometers of historic quay walls, of which much is deemed to be in poor condition. A key monitoring technique used is photogrammetry resulting in deformation testing. The fundamental data source forming the basis of this deformation analysis is a collection of overlapping images acquired of the masonry quay walls. Solely focusing on deformations overlooks a potential wealth of information which could be retrieved from this imagery, like the existence of cracks in the quay walls, a key sign of potential deformation of the structure.
As manual visual inspection of this imagery is very time-consuming, this work proposes a methodology based on fully-supervised deep learning-based segmentation techniques with the goal of detecting and localizing cracks in the masonry quay walls. For this purpose, two neural networks are trained, one for the segmentation of quay walls in images, and one for the segmentation of cracks.
The neural network architectures which are considered in this work are DeepLabV3+, FPN, MANet and LinkNet, together with different encoders and loss functions. For quay wall segmentation, we adopt transfer learning on a network trained on masonry walls and fine-tune it for quay walls specifically. Here, DeepLabV3+ with ResNeXt-50 was found to be most effective, achieving a F1-score of 96.3 % on the test set. For crack segmentation, FPN with ResNeSt-50 performed best, resulting in a test set F1-score of 78.8 %.
The inference of the crack network is done with a multi-level scheme to detect cracks at different image scales and increase output confidence.
The inherent photogrammetric properties of the imagery have proven to be vital for further post-processing steps, like aggregating overlapping predictions, resulting in more prediction confidence.
Photogrammetry also enables converting pixel-wise predictions to crack length and crack width in the units of meters and millimeters respectively. The methodology additionally proposes photogrammetric image processing methods to transform neural network predictions to a 3D representation and a true-to-scale orthographic 2D image.
Additionally a concise visual evaluation has been conducted to assess the prediction performance on an otherwise unlabelled dataset.
This thesis presents an engineering effort for fully-supervised crack localization within the context of photogrammetric processed images, with generalization in mind for automatic assessment.

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.

Historical aerial imagery serves as a valuable data source for observing Antarctica, facilitating an extended temporal scale of observation and enabling comparisons to deepen understanding of glacier dynamics. However, many historical aerial datasets, including the Antarctica Single Frames dataset utilized in this study, lack geo-referencing and orientation metadata essential for spatial analysis. One method of geo-referencing these historical images involves image matching to establish Ground Control Points (GCPs). This study focuses on the prerequisite for image matching: ensuring alignment between unreferenced historical images and already geo-referenced images in terms of scene and approximate resolution, a process termed 'geo-localization' herein.

Geo-localization is achieved by comparing the historical image with positions within a predefined geo-referenced Area of Interest (AoI). Two predefined remote sensing datasets are used: Sentinel-2 and Quantarctica Rock Outcrop Mask, from which AoIs are generated. Positions within the AoI exhibiting the highest similarity to the historical image are likely to correspond to the same ground area, thus providing the location of the historical imagery.

This similarity assessment employs two Siamese Networks: SigNet and ResNet-50. SigNet, originally designed for signature verification tasks, consists of four convolutional layers. In contrast, ResNet-50, initially developed for image classification purposes, is characterized by its deep architecture comprising approximately 50 convolutional layers, as suggested by its name. In this study, these two models are initially pre-trained on cross-domain datasets and subsequently adaptively trained with task-specific datasets created in this study. The adaptive training datasets comprise triplets of similar and dissimilar images pre-processed using methods devised in this study. An evaluation methodology based on confidence level is developed to assess the model and workflow performance, which is then applied to 51 test historical image samples.

Overall, the results indicate that the ResNet-50 based network outperforms SigNet, achieving a 95.5% average confidence level. However, the method does not meet the initial expectation of directly providing the location of the historical image within the AoI. Instead, it identifies potential locations. Nevertheless, this outcome is valuable as it streamlines the search process for subsequent image matching steps. For instance, a 95.5% average confidence level for the ResNet-50 based network correlates with an approximate 95.5% reduction in processing time for geo-referencing when integrated with image matching in subsequent steps.

Sand waves can have a significant impact on offshore activities, and with the global push for sustainability and more renewable energy sources, these negative impacts will become more prevalent. Sand waves in the North Sea have a height up to a couple of meters and a wavelength between 100 and 1000 meters. The migration or change in asymmetry of sand waves can cause free spans in export cables leading to buckling or vibration causing failure. The migration can also cause cables to be exposed on the sea bed and in danger from offshore activities like anchors and fishing boats.

This research focuses on utilizing satellite data to determine sand wave characteristics. Satellites like Sentinel-1 and Sentinel-2 which are used in this research have global availability of data over multiple years at a 10 meter resolution. Sentinel-1 has a Synthetic Aperture Radar (SAR) which creates images by sending out microwave signals and recording the strength and the time delay of the returning signals. Sentinel-2 has an optical instrument which creates images by recording the reflected light from the sun on the earth's surface. Sand waves were detected through the change in Sea Surface Roughness due to the current interaction with the sea bed as described in the Alpers-Hennings Model. The difference in resulting sand wave characteristics between SAR and optical images were determined.

Three initial areas of interest off the coast of the Netherlands were chosen based on their different characteristics. Hoek van Holland contains short irregular sand waves. Holland Kust Zuid contains long regular sand waves. The third location is Alkmaar, which contains no sand waves and was used to determine the results of the methodology when a location has no sand waves present. The first step was to determine the environmental conditions necessary for sand waves to be visible in satellite images. Image collections for an area of interest over a set period of time were filtered based on the environmental parameters. These parameters include mean glint angle and cloud cover for optical images, and wind and current speed for both optical and SAR images. Threshold values were determined through literature. The wind speed should be between 3 and 12 m/s, the current speed should be greater than 0.4 m/s, and the mean glint angle for the latitude of the North Sea is less than 56 degrees. The image collection for the year 2021 for the three different areas of interest was filtered based on these parameters. The final collection after filtering was then manually checked for the visibility of sand waves. This number was lower than the initial filtered collection which means that there are additional factors that need to be accounted for.

A methodology was created to determine the sand wave characteristics over an area by applying a Fast Fourier Transformation (FFT) and calculating the sand wavelength and wave angle from the resulting signal. This was first applied to the three different areas of interest within the North Sea, Hoek van Holland, Holland Kust Zuid, and Alkmaar. Using the resulting image collection at these locations the sand wave characteristics were calculated. Although there are no sand waves at Alkmaar, due to the methodology, sand wave characteristics are still calculated. Also, the methodology works better at Holland Kust Zuid with long regular sand waves compared to at Hoek van Holland where sand waves are shorter and irregular.

To determine at which scale the satellite images should be viewed at to obtain information on the sand wave characteristics, the data was split into different area sizes and a FFT performed. The resulting average values and spread of the sand wavelength and wave angle were compared at different area sizes for optical and SAR images, as well as Multi-Beam Sonar (MBES). This resulted in an area of 5 by 5 kilometers which allows for the correct signals to be read across different possible sand wavelengths. Also, the effect of different sources of noise on the FFT and calculation of the sand wave characteristics for optical and SAR images was determined. From this it was seen that ships within a satellite image containing sand waves introduce a very strong signal within the FFT. This prevents the sand wave characteristics from being calculated correctly. Wind farms within a satellite image affect SAR images, and not optical images. Suspended sediment transport and clouds, which only occur within optical images, have different effects. Optical images are not affected by sediment transport because the current direction will always be perpendicular to sand wave crests and therefore does not influence the calculation of sand wave characteristics. Clouds, similarly to ships, introduce a strong signal within the FFT which blocks the signal of the sand waves. Dark patches (like rain cells), which effect SAR images, block the visibility of sand waves within satellite images and therefore there no signal corresponding to sand waves within the FFT can be found.

Then, the area of interest was increased to the entire southern North Sea. Satellite images over the North Sea were downloaded in 5 by 5 km tiles for a single satellite pass over per year where the environmental parameters met the conditions necessary to view sand waves. As for the local scale cases, a FFT was applied to each tile and the sand wave characteristics were calculated including sand wavelength, wave angle, density, and spatial frequency. The differences in characteristics were compared from year to year. Typically, the difference is less than 200 meters in sand wavelength, however there are points where the difference is much larger. This is due to noise affecting the calculated value. Additionally, the data for wind speed and current speed for the date and time of the satellite image was determined. From this it is seen that optical images can contain sand waves at lower wind and current speeds, at the lower end of the boundary conditions. Sand waves are only visible in SAR images when the current speed is very high. Also, the results were compared to the water depth of the North Sea. At depths less than 40 meters which occurs on the Dutch Continental Shelf sand waves are almost always visible with the correct environmental parameters. At depths greater than 40 meters, which occurs on the West side of the sand wave field, sand waves are not visible consistently. The calculated sand wavelength over the North Sea was also compared to wavelength obtained from MBES. This resulted in low correlation values for both optical and SAR. As a result, noise present in satellite images should be taken into account.

By both applying the methodology to smaller scale cases and to the entire North Sea, it is possible to detect sand waves in different environments. Using the change in sea surface roughness due to sand waves it is possible to see sand waves in both optical and SAR satellite images. By applying the Fourier transformation the average sand wave characteristics over an area can be calculated. The methodology is limited by environmental parameters and noise that can be present in the satellite images. SAR and optical images require specific wind and current conditions, while optical images also requires specific mean glint angles and a low cloud cover. Different sources of noise also have a negative impact, adding signals to the FFT that do not correspond to the sand waves. Although the methodology proposed in this thesis is successful in determining the average sand wave characteristics over an area, more research for this topic would increase the possibilities. This includes using additional conditions for filtering image collections for sand waves, removing sources of noise, utilizing higher resolution satellite data, and testing the methodology on different sand wave fields that have different characteristics, such as symmetry. There are still many possibilities that can be explored in using satellite images to determine sand wave characteristics.

Semantic segmentation, a task vital in the creation of 3D point cloud models for buildings, is aimed at assigning meaning to individual points. However, due to the vast volume of unstructured point cloud data, precise semantic segmentation remains challenging. Significant progress has been observed in recent years with the application of deep learning techniques to point cloud segmentation, and the effectiveness of Dynamic Graph Convolutional Neural Network (DGCNN) and K-Nearest Neighbors (K-NN) in handling point cloud data has been recognized.
In this study, Dynamic Graph Convolutional Neural Network (DGCNN) was utilized for semantic segmentation on a building's point cloud scene. We adopted K-Nearest Neighbors (K-NN) as a crucial component of our methodology to optimize the segmentation process. By varying 'k' values in K-NN and exploring different block sizes, we aimed to obtain various segmentation results for comparison. When a block size of 1 meter was employed and 'k' was set to 20, an overall accuracy of 90.32%, mean accuracy of 87.64%, and IoU of 80.71% were achieved. However, the most favorable segmentation outcomes were observed when the block size remained 1 meter, and 'k' was set to 30, resulting in an overall accuracy of 93.86%, mean accuracy of 90.68%, and IoU of 84.97%.
These experiments underscore the significance of parameter selection in optimizing the performance of DGCNN for point cloud segmentation. The findings reveal that adjustments to 'k' values and block sizes can significantly influence segmentation accuracy and quality, emphasizing the importance of parameter optimization in the context of semantic segmentation for building point clouds using deep learning techniques. The utilization of K-NN played a crucial role in achieving these improvements by allowing us to adapt to the inherent variability in point cloud data.

The 3D point cloud representation of building holds significant importance in developing a comprehensive smart city model. However, due to the substantial volume of point cloud data and its inherently unstructured nature, accomplishing semantic segmentation of the point cloud poses a formidable challenge. Recent years have witnessed substantial advancements in employing deep learning techniques for point cloud segmentation. Both PointNet and PointNet++ have demonstrated their prowess in managing point cloud data. In the context of this study, we harness the potential of DGCNN for segmenting building point clouds. DGCNN plays a pivotal role in partitioning the input point cloud, thereby influencing the sensor domain's network range. Consequently, this research delves into the role of parameter k in K-NN (K-Nearest Neighbors) and its implications. The findings illustrate that augmenting the 'k' value contributes to enhancing the overall precision of DGCNN. Nonetheless, complete precision in segmenting every module cannot be assured. The research objectives in the study is building façade, and the targets are windows, walls, balconies and doors. In this study, the key parameters are the values of 'k' and the 'block size.' Experiments were conducted by varying the values of these two parameters to obtain different segmentation results of building facade point clouds. Notably, setting k to 20, block size to 1m achieves an overall accuracy rate of 89.75%, a mean accuracy rate of 85.03%, an IoU of 76.24%, while elevating k to 30 and block size to 1m results in a heightened accuracy rate of 95.74%, a mean accuracy rate of 89.37%, an IoU of 81.52%.

Unequal deformation of the soil can cause deformation or damage to buildings, like tilted facades or cracks in walls. This research investigates how deformation of a building can be analyzed using Light Detection And Ranging (LiDAR) data. Cyclomedia captures LiDAR data yearly in the Netherlands making it possible to analyze either one or multiple epochs of data. If deformation is monitored, failure can be predicted, and repairs can be performed in time.

A subsiding area is found using the Dutch surface motion map. The data points of a single building are fetched from the LiDAR point cloud to analyze the following types of deformation: a difference in the torsion and tilt angle of the facade between two epochs of data; the tilt angle of the facade for a single epoch of data; and local deformation patterns on the facade. The data is segmented before computing the angles and analyzing the local deformation patterns. During segmentation, points that do not correspond to the façade (like a sunshade or windows) are removed. After segmentation, the facade is modeled by fitting a plane using Principal Component Analysis (PCA). The plane parameters (A, B, C, D) are used to determine the torsion and tilt angles. If the torsion or tilt angles are large (above a degree), it is expected the facade has deformed. Local deformation patterns can be analyzed by using a raster containing the distance of the segmented points with an accuracy of 8 centimeters. Besides the point clouds, Cyclomedia also captures 360° panoramic images (Cycloramas). These Cycloramas can be used to explain patterns that are visible in the rasters. For example, objects in front of the building like a bench or a sunshade.

This research uses Random Sample Consensus (RANSAC) to segment the data. From the results, it can be concluded RANSAC is not very predictable because random points are taken as input. So, points corresponding to the facade can be removed instead of points corresponding to windows or doors. Therefore, it is recommended to use another segmentation method for future research instead of RANSAC. Machine learning could be a good alternative to remove objects like windows and other unwanted points from the data.

Coastal areas are appealing for human beings and are the most populated areas of the world. Therefore, coastal processes have large impacts on economy and on millions of people's lives. Not only processes such as erosion, extreme events, and flood events must be understood and monitored, but the uncertainty given by the rapid climate change on these processes and on coastal systems pose a bigger issue on these populated areas. One of the key parameters of coastal processes, which is notably required for modelling coastal floods, is the beach face slope. Unfortunately, there is a lack of a reliable global dataset of the steepness of the beach face, which has been indicated to be key limitation for developing operational coastal inundation forecasting systems, as well as for quantifying the run-up and set-up contribution at the shoreline relative to global sea-level-rise.

In this report, a methodology for providing this parameter at a global level by using a LiDAR-based satellite (ICESat-2) is presented. Here, the accuracy of the beach face slope estimation, its dependency on the angle and distance between ICESat-2 measurements (passes) and the transect perpendicular to the coastline they are projected into (transect), a spatial and temporal exploration of ICESat-2 measurements on world's coasts, as well as the limitations and opportunities of this methodology are presented.

The findings of this study reveal strong visual agreement between the beach profiles and beach face profiles obtained from the validation datasets and ICESat-2. Moreover, the computed metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), present low values, indicating a high level of agreement. The MAE values, ranging from 9 to 34 cm, not only demonstrate good agreement within the beach and beach face profiles but also indicate that the errors fall within the inherent variability of the beach. For assessing the impact of the angle and distance between passes and transect on the beach face slope extraction, synthetic data is generated using Digital Elevation Models (DEMs). The results demonstrate that long and spatially homogeneous beaches withstand larger α, whereas smaller, more variable, and pocket-shaped beaches are more sensitive to large angles due to the rapid decrease of spatial information alongshore. Nevertheless, regardless of the beach type, the closer the synthetic ICESat-2 measurements intersect over the beach face, the more likely the extracted beach face slope is to match the actual one. For both analysed cases, the beach face slope can be extracted with a tolerable level of uncertainty up to 60 degrees, and if the synthetic data is close to the beach face, the angle that it bears can be increased up to 70-80 degrees.

The novel methodology for upscaling the beach face slope extraction is validated using the Dutch yearly coastal dataset, JARKUS. The results reaffirm the dependence of the beach face slope on the angle between pass and transect. It is concluded that even though further refinement of the algorithm for demarcating the beach face is necessary, the extraction of beach face slopes can be implemented on a global scale, if some challenges are overcome. Moreover, this methodology yields a valuable byproduct in the form of cross-shore profiles, which can be extracted globally with high accuracy. This additional output enhances the utility and versatility of the methodology. Additionally, a spatial and temporal exploration of ICESat-2 ATL03 data on the world's coasts, by globally sampling boxes of 20 km², is implemented. Then, by analysing the preliminary results from boxes already processed, it is observed that these boxes can encompass anywhere from 0 to 160 passes over the course of the available 5-year data. On average, approximately 63 passes are recorded within each box. From this analysis, it is concluded that particular coastal stretches, depending on their geometry and cloud coverage, can be effectively monitored for beach face slope changes using ICESat-2. Moreover, from the information extracted from these boxes, a map is being constructed for the purpose of revealing potential areas where the extraction of beach face slopes are possible given the beach geometry and availability of data. In summary, this study provides a novel methodology that sets the basis for a new era of coastal management and monitoring using LiDAR-based satellites at a global level.

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.

Traditionally, archaeological investigations, especially archaeological remains detection, mostly depend on human observation. In order to find the objects in large areas, a lot of fieldwork has to be done and it takes a long time for archaeologists to travel around. Nowadays, the development of LIDAR provides accurate 3D geometric information, which can be used for computer-based detailed terrain study. The application of deployment of computer vision methods also provides a new idea for the automatic object detection approach.

In this study, the neural network architecture "ResNet18" was applied to airborne LiDAR data from the Western regions of Slovakia for the automated detection of undiscovered Neolithic Circular Enclosures (also called rondel in the thesis). NCEs are mysterious stone hedge-like rings scattered through Central/Eastern Europe. The LiDAR data was processed into digital rater data and realized data enhancement by the visualization technique -- Simple Local Relief Model (SLRM). Since the positive samples were limited, expanding the training dataset was crucial and was realized by data augmentation methods based on the positive samples of rondels. The augmented roundels were created by cropping the real roundels and pasting them on the new empty areas after slight modification. After that, the positive image samples and the same number of negative image samples constructed the whole data set and it was divided into two parts -- training data and test data. After the training process of ResNet18, the performances of deep learning models with different combinations of parameters were evaluated, and the selected model was applied to a large area (44276 × 29984 m2), the spatial distribution of the probabilities could be observed and 32 possible new rondel areas were chosen for further validation.

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 Earth's landscapes are shaped by processes eroding, transporting, and depositing material over various timespans and spatial scales. To understand these surface activities and mitigate potential hazards they inflict, knowledge is needed of their occurrences and properties. Near-continuous terrestrial laser scanning (TLS) enables the acquisition of point cloud time series, constituting up to thousands of three-dimensional surface morphology representations. Exploiting the full potential of this large amount of data by extracting and characterizing different types of surface activities inside these point cloud time series is, however, challenging.

This thesis addresses this challenge by developing an automated and unsupervised method for classifying 4D objects-by-change (4D-OBCs). These 4D-OBCs represent the spatiotemporal extent of individual surface activities in a point cloud time series. They are classified using a Self-organizing Map (SOM) and hierarchical clustering, grouping them into different levels of surface activity types. The workflow is tested on its ability to characterize surface activities in two study areas, a sandy beach and a snow-covered Alpine area.

Application of an optimized SOM configuration on the sandy beach results in groups of 4D-OBCs physically interpretable as different types of surface activities. A validation dataset containing 51 manually labeled 4D-OBCs of various surface activity types (e.g., intertidal bar depositions, anthropogenic bulldozer depositions) is distributed over the SOM generally according to the labels provided. The SOM thus enables the identification of 4D-OBCs displaying a particular type of surface activity, as well as subtle differences between events of one surface activity. Hierarchical clustering allows us to find and characterize broader groups of surface activities, even if the same type occurs at different points in space or time. For example, the varying spatial redistribution of sand through the initiation of different types of surface activities after the destruction of an intertidal bar system under different environmental conditions is successfully studied.

A nearly identical workflow configuration applied on the snow cover 4D-OBC set does not result in equal performance. Several groups of surface activity in the SOM contain a combination of 4D-OBCs representing different surface activity types. These results highlight the necessity of a study area specific workflow optimization by selecting specific features and SOM configurations. However, if optimized for the specific environment, the workflow has the potential to be used in long-term automated monitoring of surface activity in systems with complex morphological interactions, increasing the applicability of TLS for studying geomorphological change.

The TU Delft has acquired a UAV LiDAR system and can be used to acquire an 3D point cloud of the terrain below the system. It is of interest how such a flight mission would be best performed and what the corresponding quality would be. Current free software provided limited data quality estimation and mission planning support for the DJI Matrice 300 RTK and Yellowscan Mapper+ UAV LiDAR system. For these reasons an open source flight planner tool has been created. This tool does require the DJI Pilot 2 application and can for this reason only be used with DJI UAVs. It is optimized for the Yellowscan Mapper+ LiDAR module.

To analyze the quality of the data, two types of point cloud quality methods have been performed: comparison of targets in the point cloud to GNSS measurements, referred to the target analysis, and based on comparisons to itself for different acquisition times on the same location, referred to as the overlap analysis. For the target analysis an automatic, LiDAR intensity based method was developed, for determining target coordinates. Furthermore, target coordinates where detected manually based on image projected RGB data in the point cloud. By comparing these target coordinates to the reference GNSS target measurements, the combined GNSS and point cloud error can be estimated separate from the target fitting errors. It was found that the combined point cloud and GNSS error is likely larger than the fitting errors in up direction up to 70m flying height. This might allow for study of the point cloud error in up direction, with this method.

The presented overlap method can be used when no other reference data is available. This method divides the data in horizontal grid cells. The data in each grid cell is divided in time groups. For each time group a PCA plane is fitted and used to estimate the height in the horizontal center of the grid cell. By comparing heights between different time groups in the same grid cell, the height precision can be studied. With this method two types of overlaps are found. Within flight strips and between flight strips. The overlaps within flight strips seem to have a strong relation with the considered time difference length. This is likely caused by a combination of IMU and scan geometry errors. The overlaps between flight strips do not seem to have such a relation. This is likely caused by a combination of strip adjustment errors and possible GNSS errors. The found estimated standard deviations, up to a flying height of 70m, are generally below 17mm. It was found that flights above 70m seemed to perform significantly worse. Furthermore, grass resulted in larger estimated standard deviations than expected for low flights. This is likely caused by the ability of the scanner to measure the 3D shape of the grass leaves for lower flying heights and not for larger flying heights.

Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient grid expansion is becoming increasingly difficult. Therefore, deep learning (DL) techniques, such as convolutional neural networks (CNNs), can support collecting meta data about PV systems from aerial or satellite images, as research in the field of remote sensing has shown. However, previous research lacks the consideration of ground truth data-specific characteristics of PV panels.

This thesis aims to implement a semantic segmentation model that detects PV systems in aerial imagery to emphasize the relevance of area-specific characteristics for the training data and convolutional neural network (CNN) hyperparameters. A CNN with U-Net architecture is employed to analyze the impacts of land use types, rooftop colors, near-infrared (NIR) data, and lower-resolution images on the detection rate of PV panels in aerial imagery. The results indicate that a U-Net is suitable for classifying PV panels in high-resolution aerial images (10 cm) by reaching F1-scores of up to 91.75% while demonstrating the importance of adapting the training data to area-specific ground truth data in terms of urban and architectural properties.

The following report will enlighten to which extent LiDAR data could enhance land cover classification. It focuses on the area Lemps (26510) in Southwest France where a land cover classification was made using Sentinel-2 spectral images during the fieldwork. Using the additional LiDAR data, the focus shifts to distinguish coniferous and deciduous trees. Training data from the LiDAR data has been selected in CloudCompare. Using the training data, features for coniferous and deciduous trees were extracted in python. The unique features were used as classifiers. Features based on the Intensity were found to be important. Based on the classifiers, two methods were used to classify the area. Random Forest and Nearest Neighbour were the classification methods. The classification using Random Forest was found to be more accurate. The Random Forest classification map has been compared with previously acquired Sentinel-2 classification maps. The Corine Land Cover Classification and the classification map from the fieldwork were compared to the classification of coniferous and deciduous trees using LiDAR. Lots of overlap was found with the Corine Land Cover, some overlap was present with the map acquired during the fieldwork. The map created during the fieldwork contained less training data, hence the model was not trained enough. If more training data is collected for both LiDAR and Sentinel- 2 classifications, LiDAR data could enhance the general land cover classification. Especially taking the intensity into account as a classifier.

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.