  Despite the fact that architectural administration information in Korea has
been providing high-quality information for a long period of time, the level of
utility of the information is not high because it focuses on administrative
information. While this is the case, a three-dimensional (3D) map with higher
resolution has emerged along with the technological development. However, it
cannot function better than visual transmission, as it includes only image
information focusing on the exterior of the building. If information related to
the exterior of the building can be extracted or identified from a 3D map, it
is expected that the utility of the information will be more valuable as the
national architectural administration information can then potentially be
extended to include such information regarding the building exteriors to the
level of BIM(Building Information Modeling). This study aims to present and
assess a basic method of extracting information related to the appearance of
the exterior of a building for the purpose of 3D mapping using deep learning
and digital image processing. After extracting and preprocessing images from
the map, information was identified using the Fast R-CNN(Regions with
Convolutional Neuron Networks) model. The information was identified using the
Faster R-CNN model after extracting and preprocessing images from the map. As a
result, it showed approximately 93% and 91% accuracy in terms of detecting the
elevation and window parts of the building, respectively, as well as excellent
performance in an experiment aimed at extracting the elevation information of
the building. Nonetheless, it is expected that improved results will be
obtained by supplementing the probability of mixing the false detection rate or
noise data caused by the misunderstanding of experimenters in relation to the
unclear boundaries of windows.
