In modern warfare and airborne operations, such as those carried out by drones and helicopters, precise visibility calculations are critical to mission success. However, current automated methods for calculating line-of-sight (LoS) often rely on simplified gridded, meshed, or voxelized 2.5D representations and either ignore vegetation's impact or unrealistically model it as fully opaque. To address these limitations, this research proposes a novel method for calculating fully 3D, non-binary intervisibility directly from airborne LiDAR point clouds. The method leverages Cloud Optimized Point Cloud (COPC) streaming and k-d tree spatial indexing to rapidly query large, remote datasets. Vegetation is automatically segmented using the deep learning Myria3D framework. The algorithm computes viewsheds by modeling terrain, buildings, and semi-transparent vegetation, resulting in a continuous probability score rather than a traditional binary output. This is achieved by adapting the Beer-Lambert law to apply exponential attenuation along a discrete, volumetric LoS. Finally, the results are visualized as 3D spatial volumes or 2D map overlays for practical integration into mission planning workflows, such as safe flight height mapping and visibility-aware routing. This direct point cloud analysis bridges the gap between raw data and high-level spatial decision-making, offering a framework for safer, data-driven mission planning.
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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