License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
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DOI: 10.4230/LIPIcs.GISCIENCE.2018.40
URN: urn:nbn:de:0030-drops-93682
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9368/
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Li, Yan ; Chen, Yiqun ; Rajabifard, Abbas ; Khoshelham, Kourosh ; Aleksandrov, Mitko

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

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LIPIcs-GISCIENCE-2018-40.pdf (0.4 MB)

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.

BibTeX - Entry

@InProceedings{li_et_al:LIPIcs:2018:9368,
  author =	{Yan Li and Yiqun Chen and Abbas Rajabifard and Kourosh Khoshelham and Mitko Aleksandrov},
  title =	{{Estimating Building Age from Google Street View Images Using Deep Learning (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{40:1--40:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9368},
  URN =		{urn:nbn:de:0030-drops-93682},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.40},
  annote =	{Keywords: Building database, deep learning, CNN, SVM, Google Street View}
}

Keywords: Building database, deep learning, CNN, SVM, Google Street View
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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