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Estimating Building Age from Google Street View Images Using Deep Learning (Short Paper)

Authors Yan Li, Yiqun Chen, Abbas Rajabifard, Kourosh Khoshelham, Mitko Aleksandrov



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Author Details

Yan Li
  • Department of Infrastructure Engineering, University of Melbourne/Melbourne, Australia
Yiqun Chen
  • Department of Infrastructure Engineering, University of Melbourne/Melbourne, Australia
Abbas Rajabifard
  • Department of Infrastructure Engineering, University of Melbourne/Melbourne, Australia
Kourosh Khoshelham
  • Department of Infrastructure Engineering, University of Melbourne/Melbourne, Australia
Mitko Aleksandrov
  • Department of Infrastructure Engineering, University of Melbourne/Melbourne, Australia

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Yan Li, Yiqun Chen, Abbas Rajabifard, Kourosh Khoshelham, and Mitko Aleksandrov. Estimating Building Age from Google Street View Images Using Deep Learning (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 40:1-40:7, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.40

Abstract

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Supervised learning by regression
Keywords
  • Building database
  • deep learning
  • CNN
  • SVM
  • Google Street View

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