Identification and Extracting Method of Exterior Building Information on 3D Map

Although the Korean government has provided high-quality architectural building information for a long period of time, its focus on administrative details over three-dimensional (3D) architectural mapping and data collection has hindered progress. This study presents a basic method for extracting exterior building information for the purpose of 3D mapping using deep learning and digital image processing. The method identifies and classifies objects by using the fast regional convolutional neural network model. The results show an accuracy of 93% in the detection of façade and 91% window detection; this could be further improved by more clearly defining the boundaries of windows and reducing data noise. The additional metadata provided by the proposed method could, in the future, be included in building information modeling databases to facilitate structural analyses or reconstruction efforts.

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