  This article explores the usage of the state-of-art neural network Mask R-CNN
to be used for window detection of texture files from the CityGML model of
Berlin. As texture files are very irregular in terms of size, exposure settings
and orientation, we use several parameter optimisation methods to improve the
precision. Those textures are cropped from aerial photos, which implies that
the angle of the facade, the exposure as well as contrast are calibrated
towards the mean and not towards the single facade. The analysis of a single
texture image with the human eye itself is challenging: A combination of window
and facade estimation and perspective analysis is necessary in order to
determine the facades and windows. We train and detect bounding boxes and masks
from two data sets with image size 128 and 256. We explore various
configuration optimisation methods and the relation of the Region Proposal
Network, detected ROIs and the mask output. Our final results shows that the we
can improve the average precision scores for both data set sizes, yet the
initial AP score varies and leads to different resulting scores.
