Speaker:
Meixia Deng and Liping Di

Date(s):
03 Sep 2012 to 03 Sep 2012

Topic:

Open geospatial data, information and knowledge environments are of great importance in providing reasoned solutions to problems we face with climate change, environment protection, and other global issues and enhancing our capability in response to nature hazards and other challenges. However, problems remain in building a desired ideal environment due to technological, organizational, and cultural barriers. The key problem is that current geospatial data and information systems are not easily, adequately, or properly interoperable at the data, functions (services), systems, and semantics levels.

This presentation discusses an innovative geospatial Web service approach to solving this problem by leveraging recent advances in cyberinfrastructure (CI), Web services, and geospatial interoperability technologies. It presents an integrated system framework, interoperability mechanisms, functional requirements, and the methodology for building an open geospatial data, information and knowledge environment. It also introduces four instances of such an environment implemented in Center for Spatial Information Science and Systems (CSISS) at George Mason University (GMU): 1) GeoBrain (http://geobrain.laits.gmu.edu), 2) Global Agriculture Drought Monitoring and Forecasting System (GADMFS, http://gis.csiss.gmu.edu/GADMFS/), 3) CropScape (http://nassgeodata.gmu.edu/CropScape/), and 4)VegScape (http://dss.csiss.gmu.edu/VegScape/).

All these instances were designed and implemented as open, interoperable, data-intensive, standards-compliant, scalable, self-evolvable, maintainable, and SOA-based geospatial Web service systems, providing online and on-demand data, information and knowledge services to worldwide users. The operational uses of those instances among worldwide users have demonstrated that a Web service approach poses to be a practical and effective one for building open geospatial data, information and knowledge environments. The research results presented in this paper are able to provide visions, experiences, and technology foundations for developing more effective online data, information and knowledge environments in the near future to meet wide-range computational needs of future generations of scientists, researchers, educators and students.

 

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