Digital twin development levels include integration of geospatial data (stage 1), integration of measurement sensors (stage 2), real-time analysis, including prediction (stage 3), simulation of various scenarios, including optimisation and remote diagnostics (stage 4), and autonomous digital twin capable of learning and decision making (stage 5) [24; 25; 26]. The use of geospatial data and digital object models takes place at all levels of digital twin development and is a key element in its construction [17; 27]. Spatial models of cities and area objects are commonly created using laser scanning and digital photogrammetry [28; 29]. For areas of rivers and reservoirs, the use of a green laser beam (532 nm) for imaging the topography of the bottom is of particular importance, which is made possible by the use of Airborne Laser Bathymetry (ALB) lidar [30; 31]. This is important for shallow and non-navigable areas, where bathymetric lidar is an alternative to traditional low-density GNSS or Total Station surveys [32]. Automated mapping of bathymetry and coastal areas, including precise underwater positioning, is the subject of much research and focuses on the use of various remote sensing methods, including underwater, surface, aerial, and satellite measurements [33; 34; 35]. The fusion of bathymetric lidar data and hyperspectral imagery (HY) and bathymetric lidar and multibeam echosounder (MBES) or Side Scan Sonar have a special place in these studies [36; 37; 38; 39; 40]. Data fusion is possible, among others, through the use of special algorithms related to the study of the waveform of the laser scanner (FWF--full-waveform), the analysis of the neighbourhood of cloud points, or the study of the features of the extracted elements of the previously built spatial model [32; 36; 41; 42; 43]. Multi-source spatial data, including imagery and sensor-based observations, were processed through machine learning algorithms to enhance data reliability and provide comprehensive spatial coverage. Artificial intelligence methods, including machine learning [44; 45; 46], are also used in bottom research, mainly related to its classification. This fusion process was essential in overcoming the challenges posed by the heterogeneous nature of spatial datasets collected from different sources [10]. Ref. [13] discussed the use of multi-agent systems (MAS) for optimising the use of spatial data in water resource management. MAS facilitated the automated processing of spatial data, allowing for real-time decision making based on environmental conditions, resource availability, and consumption patterns. These methods, with regard to data fusion, generally involve two types of sensors (e.g., MBE and ALB or HY and BLS), and they make it possible to develop a point cloud in shallow-water areas, classify the bottom, or define morphological changes. Despite significant advancements, several challenges in integrating spatial data were highlighted, including inconsistencies in data formats, the need for high computa  
Figure 1: Five stages of digital twin maturity model for industrial asset management [26]. tional resources for real-time data processing, and the lack of standardised methodologies for integrating heterogeneous data sources. These barriers continue to impede the seamless integration of spatial data into water management systems, but ongoing research is addressing these gaps [10]. The paper is organised as follows. In the Introduction section, arguments to support the need for solutions to integrate spatial data in the process of building digital twins of rivers and fairways were presented. Based on the literature, research problems not yet solved effectively were detailed. Next, the method of data integration to improve the accuracy of the models created is proposed. The materials and methodology section presents the testbed, the research techniques, and methods used to achieve the research objectives. The results of the research are then presented based on processed data from the multi modal and multi-senor platforms.