## 1 Introduction
The integration of these data is crucial because spatial objects, such as buildings, infrastructure, or environmental features, need to be accurately mapped and situated in real space to enable accurate interaction with the real world. A system that provides for the integration of spatial data from georeferenced databases maintained by public administrations will allow for three important features:  
1. Data validity--The data resources in the national spatial databases are updated relatively frequently and provide high-quality spatial data, especially in the case of orthophotos. Data reliability--The data included in the national resources are produced according to specific standards, which guarantees the reliability of the data used to generate objects in the AR systems. 3. Data universality--Public administrations are obliged to make data universally accessible (requirements of the Infrastructure for Spatial Information in the European Community (INSPIRE) Directive) [30], which is implemented, for example, by carrying out photogrammetric measurements to produce an orthophotomap or by maintaining a vector database of buildings for the whole country. The objective of the research presented in this paper is to design a multimodal system that integrates data from georeferenced spatial databases to visualize geographic objects on mobile devices using AR. This study focused on selected spatial objects representing buildings. The final result of the completed process is a solid representing the building objects along with the classified roof structure. A crucial objective was to facilitate the integration of spatial data. The system employed both vector building outline data and an orthophotomap. The two aforementioned spatial data layers were incorporated into the system via the utilization of map services. In the case of the vector data, this was the WFS, while for the orthophotos it was the WCS (Web Coverage Service). This represents the initial component of the system tasked with the integration of spatial data. The key benefit of this solution is the assurance of the spatial data's validity, which is derived from georeferenced spatial databases that are updated on a regular basis. In the case of the building outlines, this occurs on a daily basis, while the orthophotomap is updated at intervals of one year or more. The data extracted using the aforementioned services provides the prepared building classification algorithm, which employs machine learning (ML) methods in YOLO 10. This constitutes the second component of the system, the purpose of which is to classify building objects into building blocks, representing them with their roof shape. The authors elected to utilize ML methodologies for the detection of roof structures, given the documented high performance of such approaches in prior research, as evidenced by the works of [PERSON] and [PERSON] [31], [PERSON] et al. [32], and [PERSON] et al. [33]. The third integral component is the visualization module of the obtained 3D models, which was implemented in an AR mobile application. The system, in its final form, merely constitutes a preliminary phase in the development of a more streamlined and integrated multimodal system. The following key achievements have been made:  
1. The integration of geospatial data with AR in real time represents a significant development in the field of geographic information science. 2. The utilization of deep learning algorithms (YOLO v10 XE) for the automated classification of roof types based on orthophotographs. 3. A proposal for future work includes the establishment of a scalable system for the management of international data. 4. The utilization of a semi-automatic approach, with the potential for progression to full automation, is imperative. This approach should be integrated with ALS LiDAR and oblique imagery.