Automatically and Accurately Conflating Satellite Imagery and Maps

There is a wide variety of geo-spatial data available on the Internet, including a number of data sources that provide satellite imagery and maps of various regions. The National Map, MapQuest, and University of Texas Map Library are good examples of map or satellite imagery repositories. In addition, a wide variety of maps are available from various government agencies, such as property survey maps and maps of oil and natural gas fields. Road vector data covering all of the United States is available from the U.S. Census Bureau. One of the key questions for Geospatial Information Systems researchers is how to accurately and efficiently align imagery, maps and vector data from these various sources. In this paper, we describe our approach to automatically and accurately align satellite imagery with the various online maps that are currently available. The traditional approach to aligning these various geospatial products is to use a technique called conflation [7], which requires identifying a set of control point pairs on the two data sources. The identification of these control points is often performed manually, which is a tedious and time-consuming process that is made even harder by the fact that many of the online sources do not even provide the coordinates of the corner points of the maps. In previous work, we developed an approach to automatically conflating road vector data with satellite imagery [2]. In this paper we describe how we address the even more challenging problem of automatically conflating maps with satellite imagery. Since we build on our previous work, we first review our approach to automatically conflate road vector data with satellite imagery. Then we describe our approach to automatically conflating a map with the satellite imagery by first using the vector data to identify all of the intersections and then utilizing a specialized point matching algorithm to align the two datasets.