## 6 Conclusions  
This paper's focus was on the development of a system that facilitates the effective integration of geophysical perimeter data with AR. A deep learning method was devised for the automatic detection of roof types from image data, specifically orthophotos. This study incorporated two roof types: gable roofs and other roof types. This enables a realistic representation of buildings and their visualization in three dimensions (3D). The results of the analyses conducted indicate the borderline statistical significance of the differences in the effectiveness of the YOLO model in classifying roof types. The metrics of precision, sensitivity, and F1-score yielded higher results for gable roofs (0.79, 0.78, and 0.78, respectively) in comparison with the other roof types (0.76, 0.75, and 0.74). The findings suggest that the model exhibits a higher degree of efficiency in recognizing objects characterized by regular geometry. The ANOVA results obtained indicate the need for further optimization of the model in the context of roof differentiation. The aim was to establish this empirically. It is recommended that the model be further refined and that thetraining dataset be expanded to encompass additional examples of roof types, with a view to enhancing the consistency and accuracy of the results. The integration of interactive 3D visualizations with AR has been shown to enhance the clarity and quality of the presentation of results to a significant degree. The utilization of libraries such as three.js and the WebXR API facilitates the dynamic exploration of building models within an AR environment. The adoption of 3D visualization techniques facilitates a more profound comprehension of the geometric disparities among edifices with disparate roof types, thereby enabling users to perform spatial analysis in an intuitive manner. The integration of such technological advancements into design processes fosters enhanced comprehension of spatial content contexts, thereby facilitating optimized architectural decision-making. Despite the advantages of the developed system, its current classification of roof types is limited to two categories. The expansion of this classification to encompass additional roof types, as well as elements such as roofing materials, roof windows, and dormers, has the potential to enhance its usability significantly. The integration of data from object-based orthophotos, oblique imagery, and ALS LiDAR could lead to improvements in the accuracy of the model and the quality of visualization [65; 66; 67; 68]. ALS LiDAR data facilitate the accurate mapping of roof heights and structures, thereby enhancing the model's capacity to differentiate between roof types with complex forms. The oblique imagery, with its varied viewing angles, provides a superior representation of roof fragments that are challenging to access and may be overlooked in conventional orthophotos. The integrity of these data sources enables the automatic generation of precise roof shapes within the system, thereby facilitating a more automated process. The integration of ALS LiDAR and oblique imagery serves to eliminate errors arising from the utilization of individual data sources, thereby enhancing the precision of roof detection and classification. Consequently, the development of a multimodal system will contribute to enhancing the accuracy and efficiency of roof recognition in diverse application contexts. The enhancement of such a system could find applications in fields as diverse as urban planning, disaster management, environmental engineering, and property management. The developed system has the potential for wide application in the fields of urban planning, architecture, and heritage management. The possibility of 3D modeling, in conjunction with AR technology, facilitates a more comprehensive spatial analysis and provides decision support for urban design and planning.