TitleVisual Estimation of Building Condition with Patch-level ConvNets
Publication TypeConference Paper
Year of Publication2018
AuthorsKoch, D., M. Despotovic, M. Sakeena, M. Döller, and M. Zeppelzauer
Conference NameProceedings of the 2018 ACM Workshop on Multimedia for Real Estate Tech
Pages12–17
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5797-5
Keywordsbuilding condition estimation, Content-based image retrieval, Deep Learning, image classification, regression models, single-family-housing, visual building analysis, visual pattern extraction
AbstractThe condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.
URLhttp://doi.acm.org/10.1145/3210499.3210526
DOI10.1145/3210499.3210526
Refereed DesignationRefereed