This study presents an automated approach to object masking in urban photogrammetry. Leveraging a pre-trained Mask R-CNN model within a Python framework, the methodology effectively removes transient objects such as vehicles and pedestrians, thereby enhancing feature matching and 3D reconstruction accuracy in complex urban environments.