  The parsing of windows in building facades is a long-desired but challenging
task in computer vision. It is crucial to urban analysis, semantic
reconstruction, lifecycle analysis, digital twins, and scene parsing amongst
other building-related tasks that require high-quality semantic data. This
article investigates the usage of the mask R-CNN framework to be used for
window detection of facade imagery input. We utilize transfer learning to train
our proposed method on COCO weights with our own collected dataset of street
view images of facades to produce instance segmentations of our new window
class. Experimental results show that our suggested approach with a relatively
small dataset trains the network only with transfer learning and augmentation
achieves results on par with prior state-of-the-art window detection
approaches, even without post-optimization techniques.
