Automatic extraction of condition-specific visual characteristics from buildings

The value of a property is influenced by a number of factors such as location, year of construction, area used, etc. In particular, the classification of the condition of a building plays an important role in this context, since each real estate actor (expert, broker, etc.) perceives the condition individually. This paper investigates automatic extraction of condition-specific visual characteristics from buildings using indoor and outdoor images as well as automatic classification of condition classes. This is a complex task because an object of interest can appear at different positions within the image. In addition, an object of interest and/or the building can be captured from different distances and perspectives and under different weather and lighting conditions. Furthermore, the classification method applied with the convolutional neural network, as described in this paper, requires a large amount of input data. The forecast results of the neural network are promising and show accuracy rates between 67 and 81% using various set-up constellations. The described method has a high development potential in the scientific as well as in the practical sense. The results are technically innovative and should, apart from research relevant contribution, make a practical contribution to future automation-supported real estate valuation procedures. The primary aim of this work is to stimulate the development of new scientifically relevant methods and questions in this direction.