Virtual Environments for Spatial Data Infrastructures on Computing Grids

Spatial Data Processing and Infrastructures are the core of future remote sensing and photogrammetry for the Earth Observation industry. For this to become an operational and production reality, quantum leap breakthroughs are to be achieved, concerning in particular image processing, error correlations, alert definitions, best usage practices, data encryption and code validation. Because problems that are expected to be orders of magnitude larger than current single discipline applications, like weather forcasting, are likely to be addressed, e.g., environmental disaster prevention and emergency management, new computing technologies are required. Among these technologies are wide area grids and distributed computing, as well as cluster and grid-based environments. It is clear that large PC-clusters and wide area grids are currently used for demanding numerical applications, e.g., nuclear and environmental simulation. It is not so clear however which approaches are currently the best for developing Spatial Data Processing and Infrastructures. A first approach takes existing grid-based computing environments and deploys, tests and analyzes Spatial Data Processing applications. A second approach executes legacy Spatial Data Processing and Infrastructures codes to characterize gridbased environments for adequate architectural hardware and software adequacy. We advocate in this paper the use of a grid-based infrastructure that is designed for a seamless approach by the users, i.e., the Spatial Data Processing and Infrastructures designers, although it relies on a sophisticated computing environments based on computing grids, i.e., wide-area computing grids, connecting heterogeneous computing resources: mainframes, PC-clusters and workstations running multidisciplinary codes and utility software, e.g., visualization tools.