In the article publications have been analyzed and summarized on this topic: studied the experience of implementing building information models and geographic information models for administrative and economic management of transport infrastructure facilities on the example of the regional airport activity. After the stage of collecting geospatial data from various sources and sensors, the data is imported into CAD-systems or GIS-systems. Different software products are used to work with both of these models. The processes of data organization in the process of creating building information models and geographic information models differ to some extent. The issues of integration of such models are not yet fully addressed and need to be addressed. In the process of integration of spatial data, solutions to the integration of semantics, topology, formats and standards of geospatial data are needed. An important task is to develop and study the experience of creating software modules that allow you to integrate BIM-models into geographic information systems. Based on the research, it is established that the current area of research is the development of technologies that allow the generation of information from BIM and GIS to create a more interconnected infrastructure. The integration of BIM and GIS information to create a spatial data infrastructure (SDI) is a perspective direction.

3D maps are becoming more and more popular due not only to their accessibility and clarity of reception, but above all, they provide comprehensive spatial information. Three-dimensional cartographic studies meet the accuracy requirements set for traditional 2D stu-dies, and additionally, they naturally connect the place where the phenomenon occurs with its spatial location. Due to the scale of the objects and difficulties in obtaining comprehensive data using only one source, a frequent procedure is to integrate measurement, cartographic, photo-grammetric information and databases in order to generate a comprehensive study in the form of a 3D map. This paper presents the method of acquiring and processing, as well as, integrating data from TLS and UAVs. Clouds of points representing places and objects are the starting point for the implementation of 3D models of buildings and technical objects, as well as for the con-struction of the Digital Terrain Model. However, in order to supplement the spatial information about the object, the geodetic database of the record of the utilities network was integrated with the model. The procedure performed with the use of common georeferencing, based on the global coordinate system, allowed for the generation of a comprehensive basemap in a three-dimensional form.

Geospatial data obtained using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) are increasingly used to model the terrain in the coastal zone, in particular in shallow waterbodies (with a depth of up to 1 m). In order to generate a terrain relief, it is important to choose a method for modelling that will allow it to be accurately projected. Therefore, the aim of this article is to present a method for accuracy assessment of topo-bathymetric surface models based on geospatial data recorded by UAV and USV vehicles. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to the public beach in Gdynia (Poland) in 2022 using a DJI Phantom 4 RTK UAV and an AutoDron USV. The geospatial data integration process was performed in the Surfer software. As a result, Digital Terrain Models (DTMs) in the coastal zone were developed using the following terrain modelling methods: Inverse Distance to a Power (IDP), Inverse Distance Weighted (IDW), kriging, the Modified Shepard’s Method (MSM) and Natural Neighbour Interpolation (NNI). The conducted study does not clearly indicate any of the methods, as the selection of the method is also affected by the visualization of the generated model. However, having compared the accuracy measures of the charts and models obtained, it was concluded that for this type of data, the kriging (linear model) method was the best. Very good results were also obtained for the NNI method. The lowest value of the Root Mean Square Error (RMSE) (0.030 m) and the lowest value of the Mean Absolute Error (MAE) (0.011 m) were noted for the GRID model interpolated with the kriging (linear model) method. Moreover, the NNI and kriging (linear model) methods obtained the highest coefficient of determination value (0.999). The NNI method has the lowest value of the R68 measure (0.009 m), while the lowest value of the R95 measure (0.033 m) was noted for the kriging (linear model) method.