## 3 Results  
### Preparation of Test Data  
The construction of the detection model constituted a pivotal component of the presented system. In order to achieve this, it was decided to utilize a conventional yet efficacious neural network model of the CNN (Convolutional Neural Network) type, such as YOLO (You Only Look Once). This model is distinguished by its rapid processing speed and high degree of accuracy, which renders it a valuable tool for the task of real-time object detection. Its single-shot architecture enables simultaneous detection and classification, significantly reducing data processing time and inference latency. Moreover, YOLO's lower memory requirements, compared with models like Faster R-CNN and EfficientDet, make it well suited for systems with constrained hardware resources. The efficiency of YOLO extends to its transfer learning capabilities, where the single-pass approach simplifies the adaptation process to new datasets. This characteristic substantially shortens the time required for model fine-tuning, ensuring faster deployment in practical applications [36, 37, 38, 39]. However, adapting the YOLO model to the intended classification necessitated a comprehensive fine-tuning process, which involved modifying the network parameters to align with the project's unique requirements. In order to successfully perform fine-tuning, it was necessary to collect an appropriately sized dataset that would form the foundation for the subsequent training process. To this end, high-resolution orthophotos with a pixel size of 0.05 m were employed, providing detailed information about the objects under analysis. In order to minimize errors resulting from limitations in accuracy, a 2 m buffer zone was established around the contours of the buildings. This is intended to compensate for any discrepancies that may exist between the geometry of the buildings and the orthophoto data. Moreover, numerical descriptions of building contours extracted from publicly available resources on the National Geocortical were employed to augment the dataset. These georeferenced data constituted a precise starting material for model training, enabling effective classification and detection of spatial objects in the system. Thanks to such data, the YOLO model could be optimized to detect and classify different building types based on their geometric features. The initial phase of this study entailed the delineation of distinct roof types through the manual identification of these features on the available orthophotos. It is anticipated that the utilization of orthophotos or quasi-orthophotos obtained from targeted unmanned aerial vehicle (UAV) flights will facilitate an enhancement in the temporal resolution of this study. Although the orthophotomap used in this study is a cartometric creation, the photographed fragments of objects above the ground are not projected in an orthogonal manner. Hence, the previously described buffer zone of 2 m was introduced around the building outlines to account for these imperfections. Each building outline was then subjected to verification and subsequently assigned a corresponding roof type. The initial selection was subsequently divided into the following roof types: flat, shed, gable, hip, pyramid, and other roof types that could not be assigned to the aforementioned categories (Figure 2). It is worthy of note that in the sample under analysis, there were instances where the outlines of buildings did not align with the data presented on the orthophotos. This was most frequently the result of situations in which the contours indicated the presence of buildings that in fact no longer existed or those that had been rebuilt or developed. Such inconsistencies may be due to delays in updating databases, dynamic urban expansion, or differences in data acquisition methodologies, indicating the need for regular verification and integration of these sources of information. Consequently, these data have been omitted from further elaboration.