Invited Speakers

  • Alexander Zipf

    University of Heidelberg,
    Germany

  • Diansheng Guo

    University of South Carolina,
    United States

  • Diego G. Loyola R.

    German Aerospace Center (DLR),
    Germany

  • Wenwen Li

    Arizona State University,
    United States

  • Alexander Zipf

    Professor

    Department of Geography

    University of Heidelberg

    Website:
    http://www.geog.uni-heidelberg.
    de/personen/gis_zipf.html

    Title: Analyzing Volunteered Geographic Information (VGI)
    with Big Data Technologies

    Abstract: User Generated Geographic Information on the Web provides more and more global datasets of unprecedented volume and spatial and temporal resolution. This poses challenges to spatio-temporal analytics of such data. In particular the temporal dimension becomes more and more important for the understanding, quality-assessment and usage of this new type of geographic information. Therefore we need to develop new approaches for data management, processing and analysing of big VGI data based on big data technologies. These are applying e.g. in-memory databases and cloud computing. Examples will be given based on use cases for quality analysis and improvement of OpenStreetMap (OSM) history data...
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  • Diansheng Guo

    Associate Professor

    Department of Geography

    University of South Carolina

    Website: http://spatialdatamining.org

    Title: Spatial Data Mining, Geovisualization, and Big Data Analytics—Challenges, Methods and Applications

    Abstract: Spatial and spatiotemporal data analytics plays a major role in this era of Big Data as the majority of data available today are inherently spatial, collected with ubiquitous location-aware sensors such as mobile apps, GPS, census survey, satellites, monitor stations, social media, and geocoded health records. The key to unlock the value of such data lies in the development and application of innovative theories and methodologies to extract information, discover new knowledge and produce actionable solutions from big spatial data. However, it remains a challenging research problem to analyze, visualize and understand complex and dynamic geospatial data, which come in different forms, with various quality issues, for different purposes, and involving many known/unknown factors. In ...
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  • Diego G. Loyola R.

    Dr.-Ing.

    German Aerospace Center (DLR)

    Website: http://atmos.caf.dlr.de/gome2/

    Title: Mastering the Big Data from Atmospheric Composition Satellite Sensors

    Abstract: Air pollution has become by far the leading environmental health risk factor and satellite sensors are crucial for monitoring the air quality on a global scale. This talk presents machine learning techniques for mastering the expected Big Datafrom the new generation of European atmospheric composition Copernicus satellite missions (Sentinel-5 Precursor, Sentinel-4, and Sentinel-5) with increased spatial, temporal, and spectral resolution.We developed a systematic and comprehensive method for optimally handling regression tasks with very large high dimensional data. The proposed approach is based on smart sampling techniques for minimizing the number of samples to be generated by using an iterative...
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  • Wenwen Li

    Associate Professor

    School of Geographical Sciences and Urban Planning

    Arizona State University

    Website: http://www.public.asu.edu/~
    wenwenl1/

    Title: Lowering the barriers for accessing distributed geospatial big data to advance spatial data science: the PolarHub solution

    Abstract: Data is the crux of science. The widespread availability of big data today is of particular importance for fostering new forms of geospatial innovation. This paper reports a state-of-the-art solution that addresses a key cyberinfrastructure research problem—providing ready access to big, distributed geospatial data resources on the Web. We first formulate this data-access problem and introduce its indispensable elements, including identifying the cyber-location, space and time coverage, theme, and quality of the dataset. We then propose strategies to tackle each data-access issue and make the data more discoverable and usable for geospatial data users and decision makers. Among these strategies is large-scale web crawling as a key technique to support automatic collection of online geospatial data that are highly distributed...
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