2 Search Results for "Chen, Yan"


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

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

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)


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.

Cite as

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)


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@InProceedings{li_et_al:LIPIcs.GISCIENCE.2018.40,
  author =	{Li, Yan and Chen, Yiqun and Rajabifard, Abbas and Khoshelham, Kourosh and Aleksandrov, Mitko},
  title =	{{Estimating Building Age from Google Street View Images Using Deep Learning}},
  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 =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.40},
  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}
}
Document
Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens

Authors: Yan Chen, Maxwell Harper, Joseph Konstan, and Sherry Li

Published in: Dagstuhl Seminar Proceedings, Volume 7271, Computational Social Systems and the Internet (2007)


Abstract
We explore the use of social comparison theory as a natural mechanism to increase contributions to an online movie recommendation community by investigating the effects of social information on user behavior in an online field experiment. We find that, after receiving behavioral information about the median user's total number of movie ratings, users below the median demonstrate a 530% increase in the number of monthly movie ratings, while those above the median decrease their monthly ratings by 62%. Movements from both ends converge towards the median, indicating conformity towards a newly-established social norm in a community where such a norm had been absent. Furthermore, the social information has a more dramatic effect on those below the median, suggesting an interaction between conformity and competitive preferences. When given outcome information about the average user's net benefit score from the system, consistent with social preference theory, users with net benefit scores above average contribute 94% of the new updates in the database. In both treatments, we find a highly significant Red Queen Effect.

Cite as

Yan Chen, Maxwell Harper, Joseph Konstan, and Sherry Li. Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens. In Computational Social Systems and the Internet. Dagstuhl Seminar Proceedings, Volume 7271, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


Copy BibTex To Clipboard

@InProceedings{chen_et_al:DagSemProc.07271.14,
  author =	{Chen, Yan and Harper, Maxwell and Konstan, Joseph and Li, Sherry},
  title =	{{Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens}},
  booktitle =	{Computational Social Systems and the Internet},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7271},
  editor =	{Peter Cramton and Rudolf M\"{u}ller and Eva Tardos and Moshe Tennenholtz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.07271.14},
  URN =		{urn:nbn:de:0030-drops-11550},
  doi =		{10.4230/DagSemProc.07271.14},
  annote =	{Keywords: Social comparison, conformity, public goods, embedded online field experiment}
}
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