Knowledge Management Over Time-Varying Geospatial Datasets

Project Url:
Project Contact Person: Peggy Agouris (207) 581 2180
Sponsoring Institution: University of Maine
Principal Investigator: Peggy Agouris

Anthony Stefanidis - University of Maine
Kate Beard - University of Maine
Vassilis Tsotras - University of California - Riverside
Mark Gahegan - Pennsylvania State University

Research Partners:

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.

Abstract: 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.