In the article data fusion techniques for underwater environment modelling are presented. For better understanding the problem, authors divided data fusion techniques into three parts, in reference to the output information that needed to be obtained.

The popularity of mobile devices is growing also in the field of water navigation. One of the disadvantages of them however is lack of hardware possibilities for direct connection of external sensors. The paper provides discussion on the possibilities of external sensors using in mobile navigation system for inland shipping. The analysis of data and sensors availability in this filed is followed with data model definition. Then various possibilities of integration of spatial data in the system are presented, including typical for GIS, geoinformatic web services approach.

The paper presents considerations on presenting of radar data in mobile systems used for navigation in inland waters. The concept of using modern achievements of mobile cartography for this purpose is given. Such approach allows enhancing and facilitating of radar information interpretation, especially for unexperienced users. Short analysis of radar information in inland navigation is included, followed by a concept of using mobile cartography for integration of radar information into inland navigational system.

Digital twin is an attractive technology for the representation of objects due to its ability to produce precise measurements and their geovisualisation. Of special interest is the application and fusion of various remote sensing techniques for shallow river and inland water areas, commonly measured using conventional surveying or multimodal photogrammetry. The construction of spatial digital twins of river areas requires the use of multi-platform and multi-sensor measurements to obtain reliable data of the river environment. Due to the high dynamics of river changes, the cost of measurements and the difficult-to-access measurement area, the mapping should be large-scale and simultaneous. To address these challenges, the authors performed an experiment using three measurement platforms (boat, plane, UAV) and multiple sensors to acquire both cloud and image spatial data, which were integrated temporally and spatially. The integration methods improved the accuracy of the resulting digital model by approximately 20 percent.