Accurate and efficient integration of geospatial data is an important problem with applications in areas such as emergency response and urban planning. Some of the key challenges in supporting large-scale geospatial data integration are automatically computing the quality of the data provided by a large number of geospatial sources and dynamically providing high quality answers to the user queries based on a quality criteria supplied by the user. We describe a framework called the Quality-driven Geospatial Mediator (QGM) that supports efficient and accurate integration of geospatial data from a large number of sources. The key contributions of our framework are: (1) the ability to automatically estimate the quality of data provided by a source by using the information from another source of known quality, (2) representing the quality of data provided by the sources in a declarative data integration framework, and (3) a query answering technique that exploits the quality information to provide high quality geospatial data in response to user queries. Our experimental evaluation using over 1200 real-world sources shows that QGM can accurately estimate the quality of geospatial sources. Moreover, QGM provides better quality data in response to the user queries compared to the traditional data integration systems and does so with lower response time.

The rapid increase in the availability of geospatial data has motivated the effort to seamlessly integrate this information into an information-rich and realistic 3D environment. However, heterogeneous data sources with varying degrees of consistency and accuracy pose a challenge to such efforts. We describe the geospatial decision making (GeoDec) system, which accurately integrates satellite imagery, three-dimensional models, textures and video streams, road data, maps, point data and temporal data. The system also includes a glove-based user interface