We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By closing this message, you are consenting to our use of cookies.

Spatiotemporal Data Fusion for Urban Studies

International Journal of Image and Data Fusion (Special Issue)

In this big data era, huge amount of spatiotemporal data has been acquired in various ways, including remote sensing images, social media, check-in records, taxi trajectory, videos, street view images, as well as in-situation and census data. All such spatiotemporal data are usually heterogeneous in terms of data format, measurement method and representation form, and they may be obtained at different spatial, temporal and semantic scales. Despite the increasing availability of spatiotemporal data, sophisticated data fusion methods and novel applications in urban environments are still limited.

Since more than half of the world population lives in urban areas, rapid urbanization has brought with it many environmental and social problems, e.g., urban heat islands, air/water pollution, environmental quality degradation, increased energy consumption and greenhouse gases emissions, and the transformation from natural vegetation to impervious surfaces. Thus, there have been growing efforts on creating a sustainable urban environment, alleviating the negative impacts of urbanization and improving living quality and human well- being in cities. These, however, require the support of diverse types of spatiotemporal data and associated novel techniques for fusing and integrating them.

This special issue calls for innovative techniques for fusing diverse spatiotemporal data as well as novel applications in urban studies. The topics include but are not limited to:

  • Pixel-, feature- and decision-level fusion algorithms and methodologies
  • Spatiotemporal data fusion methods
  • Conflation technologies of multi-source, multi-scale and multi-temporal data
  • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets
  • Deep learning and spatiotemporal statistical methods for data fusion
  • Land-cover and land-use information extraction
  • Change detection and dynamic analysis based spatiotemporal data
  • Data fusion methods for urban environmental information extraction, including urban buildings, urban roads, urban green space, greenhouse gases emissions, urban radiation, urban temperature, urban energy consumption, and pollution
  • Applications of fusion methods in urban ecology, urban landscape, urban morphology, and urban biodiversity
  • Evaluation indicators of sustainable development, living quality and human well-being.

Submission guidelines

Manuscripts should be submitted online at: http://mc.manuscriptcentral.com/tidf by June 30, 2019. Research articles, review articles as well as letters in this area are invited. Paper should be prepared following the Instructions for authors which can be found on the journal website:

https://www.tandfonline.com/action/authorSubmission?journalCode=tidf20&page=instructions.

The papers will undergo a double-blind peer review process. As soon as accepted, papers will be firstly published online continuously and the first six papers accepted will be listed together as the Special Issue. All other papers that are subsequently accepted will be included in the regular issues of the journal.

There is no charge for publication in this journal.

Guest Editors

Jixian Zhang, Professor, National Quality Inspection and Testing Center for Surveying and Mapping Products, Beijing,China.
E-Mail: zhangjx@casm.ac.cn

Shihong Du, Associate Professor, Institute of Remote Sensing and GIS, Peking University, Beijing, China.
E-Mail: shdu@pku.edu.cn

BoHuang, Professor, Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
E-Mail: bohuang@cuhk.edu.hk

Desheng Liu, Associate Professor, Department of Geography, The Ohio State University, 154 North Oval Mall, Columbus, USA.
E-Mail: liu.738@osu.edu

International Journal of Image and Data Fusion

International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications.

Visit Journal