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http://www.spatial.maine.edu/~peggy/dgi.html
Peggy
Agouris (207) 581 2180
University
of Maine
Peggy
Agouris
Anthony
Stefanidis - University of Maine
Kate Beard - University of Maine
Vassilis Tsotras - University of California - Riverside
Mark Gahegan - Pennsylvania State University
Richard
Berg - National Imagery and Mapping Agency (NIMA)
George Hanuschak - USDA National Agricultural Statistics Service (NASS)
Dale Bartin - BAE Systems
Cliff Kottman - Open GIS Consortium, Inc.
Geospatial datasets are collected and processed by a variety of Federal
Agencies. Such data and the information contained therein are of use
to a practically limitless array of Federal and State Agencies, and
private companies. Advancements in sensor technology, computer hardware
and software have resulted in the availability of huge amounts of diverse
types of geospatial datasets. Our objective in this project is to facilitate
the integration of those datasets across space and time, and to improve
knowledge management over such time-varying geospatial datasets. In
doing so, we will improve accessibility to the information they contain,
making it more useful to groups of users that are constantly increasing
and diversifying. In this project we are dealing specifically with four
complementary challenging research issues which are keys to realizing
the integration and improved access to the information content of heterogeneous
time-varying geospatial datasets. Specifically, we address: * The development
of a geospatial knowledge management framework to provide the syntax,
context, and semantics for researching, understanding, and leveraging
technical and human behaviors related to spatial understanding and work.
* The development of novel meta-information concepts to convey summaries
of heterogeneous datasets (focusing especially on raster and vector
spatial datasets). This is a step towards next generation geospatial
metadata, where we take advantage of modern computer capabilities to
convey the actual content of datasets. * The development of efficient
techniques for discovering sequential patterns in spatio-temporal data
sets. Sequential patterns are important as they take into account not
only the spatial characteristics of a sequential event but also the
time order by which the event components happened. * The integration
of the above issues to support spatio-temporal reasoning for the extraction
of complex information through scene modeling and analysis processes.
In order to address the above issues, this project reflects the collaboration
of academic sites, federal agencies, and industrial partners. The academic
sites (University of Maine, the National Center for Geographic Information
and Analysis, University of California Riverside, and The Pennsylvania
State University) offer a combination of expertise in computer and information
science, databases, digital image analysis, and geographic information
science. The involved federal agencies (National Imagery and Mapping
Agency - NIMA, US Department of Agriculture: National Agricultural Statistics
Service - USDA NASS, US Army Topographic Engineering Center - TEC, Federal
Geographic Data Committee -FGDC), and industrial partners (BAE Systems,
The Open GIS Consortium, Inc. - OCG, Inc.) are major producers, users,
and providers of geospatial datasets. By addressing the above issues
during the course of this project we will advance science through: *
the development of an integrated environment for spatio-temporal datasets
that can handle the needs of diverse users, and * the development of
novel ways to access, represent, and visualize the information content
of such an integrated environment, thus optimizing its use. At the same
time, we are addressing the evolving needs of diverse governmental geospatial
information science communities.
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