2026-07-23T16:35:26Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9368 2024-03-06T10:44:09Z ddc:004 open_access

Estimating Building Age from Google Street View Images Using Deep Learning (Short Paper) Li, Yan Chen, Yiqun Rajabifard, Abbas Khoshelham, Kourosh Aleksandrov, Mitko Building database deep learning CNN SVM Google Street View Building databases are a fundamental component of urban analysis. However such databases usually lack detailed attributes such as building age. With a large volume of building images being accessible online via API (such as Google Street View), as well as the fast development of image processing techniques such as deep learning, it becomes feasible to extract information from images to enrich building databases. This paper proposes a novel method to estimate building age based on the convolutional neural network for image features extraction and support vector machine for construction year regression. The contributions of this paper are two-fold: First, to our knowledge, this is the first attempt for estimating building age from images by using deep learning techniques. It provides new insight for planners to apply image processing and deep learning techniques for building database enrichment. Second, an image-base building age estimation framework is proposed which doesn't require information on building height, floor area, construction materials and therefore makes the analysis process simpler and more efficient. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Yan Li and Yiqun Chen and Abbas Rajabifard and Kourosh Khoshelham and Mitko Aleksandrov 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.40 urn:nbn:de:0030-drops-93682 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.40 eng https://creativecommons.org/licenses/by/3.0/legalcode