HR: 1340h
AN: IN53A-0947    [Abstracts]
TI: Information Semantic Tools for Coastal Data Management
AU: * Durbha, S S
EM: suryad@gri.msstate.edu
AF: Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, Mississippi State, MS 39762, United States
AU: King, R L
EM: rking@engr.msstate.edu
AF: Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, Mississippi State, MS 39762, United States
AU: Younan, N H
EM: younan@ece.msstate.edu
AF: Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, Mississippi State, MS 39762, United States
AU: Rajender, S K
EM: santosh@gri.msstate.edu
AF: Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, Mississippi State, MS 39762, United States
AU: Bheemireddy, S
EM: shruthi@gri.msstate.edu
AF: Department of Electrical and Computer Engineering, GeoResources Institute (GRI), Mississippi State University, Mississippi State, MS 39762, United States
AB: In a coastal disaster event, it is necessary to obtain information about water level (depth), winds, currents, waves, temperature-salinity stratification in real time and predictions of water level (12-24 hrs), storm surge (48-72 hours) in advance. It has been estimated that better preparation, response, and mitigation will reduce average costs of storm-related disasters by 10%. The dissemination of information that is time critical calls for systems that will facilitate quick assessment of the scenario from multiple perspectives. Sensor data are obtained from a multitude of distributed sensor networks. Our current work funded by Northern Gulf Institute (NGI) on Sensor Web tools for coastal buoys based on OGC sensor web enablement framework enables the use of real or near real time data derived from coastal sensor networks and dynamic selection and aggregation of multiple sensor systems, meteorological and oceanographic simulations and other decision support systems in a web services- based environment. In addition, we pursue the semantic web approaches to understand the context of the data, resolve the meaning, interpretation or usage of the same or related data and develop knowledge-based tools for access to the information sources. Observations from satellites provide a variety of measurements that are not otherwise available or affordable. However, the use of such valuable information in a rapid assessment scenario is hindered by the fact that it is cumbersome to explore huge image databases through manual or semi automated methods. The Rapid Image information mining (RIIM) tool that we developed for this purpose is demonstrated with imagery data from Landsat ETM+ of post Katrina hurricane.
DE: 0525 Data management
DE: 0540 Image processing
DE: 1848 Monitoring networks
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting