The performance of the Mask R-CNN model for instance segmentation of ceramic rooftops was evaluated using a high-resolution orthomosaic generated from UAV-based photogrammetry. Model training and inference were performed in ArcGIS Pro 3.5.3 with a ResNet-50 backbone. The model demonstrated high detection reliability, achieving a Precision of 96.62%, a Recall of 78.81%, and an F1-score of 86.81% at an Intersection over Union (IoU) threshold of 0.5. Most omission errors were associated with light-colored, elongated rooftops, highlighting limitations in the representativeness of the training sample and morphological variability. Fragmentation of larger rooftops into multiple segments was also observed, which affected accuracy metrics. To address this, a topological post-processing step was implemented to merge overlapping polygons, thereby improving segmentation consistency. These results indicate that Mask R-CNN is effective for high-resolution rooftop mapping, especially in applications requiring high precision. The approach is operationally feasible and transferable to similar datasets, enabling scalable analyses. It serves as a complementary tool for urban mapping, supporting the monitoring of urban dynamics and the analysis of construction patterns related to building standards and socioeconomic conditions.