Heritage point clouds are an increasingly common tool used in the conservation and documentation of cultural assets. Yet, their geometric complexity, uniqueness, and scale make automated processing using machine learning or deep learning difficult. Mathematical morphology offers an alternative using geometric assumptions in the form of a structuring element, requiring no training data and remaining interpretable. This thesis extends the point-based mathematical morphology algorithms proposed by Balado et al. (2020) to make them useful for real-world heritage point cloud processing. The original algorithms are optimized using a parallel implementation on the GPU in the form of compute shaders, reducing the time complexity of both erosion and dilation to O(n) and achieving a speedup of approximately 100x over the sequential baseline. Four extensions to the algorithms are created: a continuous erosion score replacing the binary output, orientation-aware operations using TNB matrix rotation with both a predetermined and a brute-force variant, density-aware structuring elements in discrete and continuous forms, and a set of helper functions enabling compound morphological operations. These contributions are evaluated on three heritage applications. Segmentation using sequential morphological opening and hit-or-miss transform achieves a mIoU of 0.623 and an overall accuracy of 0.813 on the Images&PointClouds Cultural Heritage Dataset without parameter optimization. Object detection achieves F1 scores above 0.920 on the Fontana dei Mesi dataset. Gap filling via morphological closing performs well locally but is sensitive to the geometric complexity of the scene when applied globally, with edge detection and template matching providing more consistent results. The results show that point-based mathematical morphology, when extended with the contributions of this thesis, is a practical and effective tool for heritage point cloud processing that does not require annotated training data. ... Read more

To help prevent flooding of rivers and cities, Dutch maritime contractor Van Oord regularly dredged 52 side channels as part of the Dutch Department of Waterways and Public Works' (Rijkswaterstaat) "Room for Rivers" strategy. Side channels make rivers more resilient to flooding by providing increased flow capacity, buffer space, and a secondary path downstream for water. Van Oord wishes to know how they can better leverage their growing historical data collection to enable predictive maintenance of side channels in the form of dredging.
Instead of developing a complex hydrological model, which would require deep knowledge of river morphology. We, as Geomatics students, extracted insights directly from the available geospatial data. For our 10-week MSc Geomatics Synthesis Project, our main research question is as follows: "How can the features of a side channel be identified and extracted to enable predictive maintenance?"
In order to answer this question for our client Van Oord, we performed a literature review and interviewed domain experts to identify relevant characteristics of side channels. Then, we explored the available geo-spatial data to determine which characteristics can be modeled as features, before processing the data in an FME pipeline to calculate these feature values in an automated, extendible, and understandable way. These features were then stored in a geo-spatial database. Reading from this database, we created a prototype machine learning model that takes the features as input. The model enables analysis of the side channels to derive insights into the sedimentation of side channels, reaching 84% accuracy within a 5cm error for the Bakenhof channel.

The result is a robust FME-based data processing pipeline, a geo-spatial database with 19 unique features for 26 suitable side channels, and a prototype neural network showing significant predictive ability. The product enables the client to better estimate side channel behavior, enabling informed predictive maintenance, as well as allowing the client to better decide moments when expensive channel measurements can be skipped. ... Read more