## 4 Discussion  
The findings of this study contribute significantly to the ongoing development of spatial digital twins, particularly in integrating diverse spatial datasets for water and land-based systems. Through the use of data from aerial laser scanning (ALS), mobile laser scanning (MLS), and multi-beam echo sounders (MBES), the study achieved a precision improvement of 0.1 to 0.2 m in bathymetric measurements, a more than 20% enhancement compared to traditional methods. This was made possible by implementing a combination of time synchronisation and spatial alignment algorithms, underscoring the value of precise temporal-spatial integration. Such levels of accuracy are essential for tasks like flood prevention, navigation safety, and infrastructure maintenance, particularly in dynamic inland waterways.  
Temporal integration, where timestamps were harmonised across different platforms, emerged as a key innovation, significantly enhancing the initial data fusion accuracy from several centimetres to just a few millimetres. Similarly, spatial integration ensured point cloud consistency within a margin of 0.1 m. The Q-ST transformation algorithm proved superior to traditional Helmert transformations, reducing the number of required points for accurate alignment and optimising computational resources without sacrificing precision. Validation exercises confirmed these improvements, with field control points showing a mean deviation error of just 0.1 m.  
These advancements align with existing research on digital twins where spatial data forms the foundational building block. For example, studies have used LiDAR data to create digital twins of bridges for virtual-reality-based inspections [51], to assess geometric quality in building facades [52], and to monitor large-scale structures like wind turbines [53]. Each of these applications emphasises the importance of regular updates to reflect the current physical state of assets and their environmental context. Similarly, urban-scale digital twins, such as those in [54; 55; 56], rely on LiDAR and other geospatial data sources for modeling city objects and ensuring real-time monitoring.  
Figure 15: Accuracy of green lidar measurements based on control measurements. Values centred around 0; error magnitudes are shown on a scale appropriate to the Z-axis. Uncertainty of coordinates XY (a), XZ (b), YZ (c). Histogram of uncertainty X (d), Y (e), histogram 2D of XY (f).  
Our study distinguishes itself by integrating geospatial data not as an end but as a means to enrich behavioural and contextual understandings of cyber-physical systems (CPS). Unlike approaches focusing solely on static geospatial information [51; 52; 55], our methodology highlights system behaviours over time captured and structured to support lifecycle perspectives and stakeholder-specific business processes. For example, studies like [57; 58] have shown the potential of integrating LiDAR data with other datasets for dynamic applications like power line inspections and vegetation management. Our work extends these principles by combining temporal, spatial, and semantic dimensions, thus advancing towards intelligent, behaviour-centred digital twins.  
Despite these successes, challenges remain. Processing large datasets requires substantial computational power, and discrepancies between horizontal and vertical resolutions persist, especially in shallow water areas. Future research should address these limitations by refining algorithms for dataset alignment and exploring more efficient data fusion techniques. Methodologies like those proposed in [59; 60; 61; 62; 63; 64] offer valuable insights for addressing these challenges. For instance, automated deployment methods [62] and life-cycle-oriented design models [64] could enhance our approach, particularly for continuous updates and real-time monitoring.