We present an automated classification method for global urban area mapping by integrating satellite images taken by Visible and Near-Infrared Radiometer of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER/VNIR) and GIS data derived from existing urban area maps. The method consists of two steps. First, we extracted urban areas from ASTER/VNIR satellite images by using an iterative machine-learning classification method known as Learning with Local and Global Consistency (LLGC). This method is capable of automatically performing classification with a noisy training dataset, in our case, low-resolution urban maps. Therefore, we were able to perform supervised classification of ASTER/VNIR images without using labor-intensive visual interpretation. Second, we integrated the LLGC confidence map with other maps by logistic regression. The logistic regression complemented misclassifications in the LLGC map and provided useful information for further improvement of the model. In an experiment including 194 scenes of ASTER/VNIR images, the integrated maps were developed at a resolution of 15 m resolution, which is much finer than existing maps with resolutions of 300 to 1000 m. The maps achieved an overall accuracy of 90.0% and a kappa coefficient of 0.565, both of which are higher than or almost equal to the values for major existing global urban area maps.

As the Earth's ecosystem is a spatially and temporally complex system by nature, it is not sufficient to observe such events and phenomena locally; problems must be solved on a global scale. Therefore, the accumulation of knowledge about the Earth in various forms and a scientifically correct understanding of the Earth are necessary. The authors have been leading the ldquoGlobal Earth Observation (GEO) Gridrdquo project since 2005, which is primarily aimed at providing an e-Science infrastructure for the worldwide Earth sciences community. In the community, there are wide varieties of existing datasets including satellite imagery, geological data, and ground sensed data that each data owner insists own licensing policy. Also, there are so many related projects that will be configured as a virtual organization (VO) enabled by Grid technology. The GEO Grid is designed to integrate all of the relevant data virtually, again enabled by Grid technology, and is accessible as a set of services. In this paper, first we describe design principles of the GEO Grid that are determined based on accommodating users requirements for publishing, managing, and using data. Second, software architecture and its preliminary implementations are specified where we take the Grid computing and Web service technologies as the core components that comply with a standard set of technologies and protocols. In addition, GEO Grid has been recognized to contribute to GEO or Global Earth Observation System of Systems (GEOSS) as a part of the Japanese government's commitment.