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project
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Project Award Information:
Award Number: EIA-9983445
Duration: Aug.
1, 2000 - July 30, 2003
Project Summary:
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.
We are focusing on the identification of similarities
in behavioral patterns and
the establishment of causality.