2 Search Results for "Mai, Gengchen"


Document
Short Paper
Talk of the Town: Discovering Open Public Data via Voice Assistants (Short Paper)

Authors: Sara Lafia, Jingyi Xiao, Thomas Hervey, and Werner Kuhn

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)


Abstract
Access to public data in the United States and elsewhere has steadily increased as governments have launched geospatially-enabled web portals like Socrata, CKAN, and Esri Hub. However, data discovery in these portals remains a challenge for the average user. Differences between users' colloquial search terms and authoritative metadata impede data discovery. For example, a motivated user with expertise can leverage valuable public data about transportation, real estate values, and crime, yet it remains difficult for the average user to discover and leverage data. To close this gap, community dashboards that use public data are being developed to track initiatives for public consumption; however, dashboards still require users to discover and interpret data. Alternatively, local governments are now developing data discovery systems that use voice assistants like Amazon Alexa and Google Home as conversational interfaces to public data portals. We explore these emerging technologies, examining the application areas they are designed to address and the degree to which they currently leverage existing open public geospatial data. In the context of ongoing technological advances, we envision using core concepts of spatial information to organize the geospatial themes of data exposed through voice assistant applications. This will allow us to curate them for improved discovery, ultimately supporting more meaningful user questions and their translation into spatial computations.

Cite as

Sara Lafia, Jingyi Xiao, Thomas Hervey, and Werner Kuhn. Talk of the Town: Discovering Open Public Data via Voice Assistants (Short Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 10:1-10:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{lafia_et_al:LIPIcs.COSIT.2019.10,
  author =	{Lafia, Sara and Xiao, Jingyi and Hervey, Thomas and Kuhn, Werner},
  title =	{{Talk of the Town: Discovering Open Public Data via Voice Assistants}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{10:1--10:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.10},
  URN =		{urn:nbn:de:0030-drops-111026},
  doi =		{10.4230/LIPIcs.COSIT.2019.10},
  annote =	{Keywords: data discovery, open public data, voice assistants, essential model, GIS}
}
Document
xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts

Authors: Bo Yan, Krzysztof Janowicz, Gengchen Mai, and Rui Zhu

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


Abstract
With recent advancements in deep convolutional neural networks, researchers in geographic information science gained access to powerful models to address challenging problems such as extracting objects from satellite imagery. However, as the underlying techniques are essentially borrowed from other research fields, e.g., computer vision or machine translation, they are often not spatially explicit. In this paper, we demonstrate how utilizing the rich information embedded in spatial contexts (SC) can substantially improve the classification of place types from images of their facades and interiors. By experimenting with different types of spatial contexts, namely spatial relatedness, spatial co-location, and spatial sequence pattern, we improve the accuracy of state-of-the-art models such as ResNet - which are known to outperform humans on the ImageNet dataset - by over 40%. Our study raises awareness for leveraging spatial contexts and domain knowledge in general in advancing deep learning models, thereby also demonstrating that theory-driven and data-driven approaches are mutually beneficial.

Cite as

Bo Yan, Krzysztof Janowicz, Gengchen Mai, and Rui Zhu. xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 17:1-17:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Copy BibTex To Clipboard

@InProceedings{yan_et_al:LIPIcs.GISCIENCE.2018.17,
  author =	{Yan, Bo and Janowicz, Krzysztof and Mai, Gengchen and Zhu, Rui},
  title =	{{xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{17:1--17:15},
  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.2018.17},
  URN =		{urn:nbn:de:0030-drops-93450},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.17},
  annote =	{Keywords: Spatial context, Image classification, Place types, Convolutional neural network, Recurrent neural network}
}
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