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

Window detection in aerial texture images of the Berlin 3D CityGML Model