TitleTowards Automated Real Estate Assessment from Satellite Images with CNNs
Publication TypeConference Paper
Year of Publication2017
AuthorsMuhr, V., M. Despotovic, D. Koch, M. Döller, and M. Zeppelzauer
Conference NameIn Proceedings of the 10th Forum Media Technology (FMT), Best Paper Award
Volume2009
Pages14–23
Date Published11/2017
PublisherCEUR Workshop Proceedings
Conference LocationSt. Pölten, Austria
KeywordsClassification, CNNs, Convolutional Neural Netwiorks, Deep Learning, Image Analysis, pattern recognition, Real Estate Image Analysis, Satellite Image Analysis
AbstractA driving factor for real estate prices is the location quality. Models for location quality are usually built from available price information and distinct GIS information. In this paper, we present a first approach towards the automated assessment of location quality from satellite images using computer vision. For this purpose, we first introduce a novel dataset generated from publicly available data sources with suitable ground-truth annotations for location assessment. Next, we adapt a state-of-the-art convolutional neural network (CNN) and adapt it to predict different land covers and objects from satellite images. Finally, we feed information derived from the recognized land covers into a regression-based price model which acts as a proxy for the assessment of location quality. Our results show that (i) land cover classification can be performed with high accuracy and demonstrates that automatic classification could further be used in the future for the detection of mis-aligned and erroneous GIS data; (ii) our adapted network reaches state-of-the-art performance in much less training time compared to our reference network; (iii) the automatically extracted visual information improves the prediction of real estate prices and thereby shows clear potential for the description of location quality.
URLhttp://ceur-ws.org/Vol-2009/fmt-proceedings-2017-paper1.pdf
Refereed DesignationRefereed