Automatic and Accurate Extraction of Road Intersections from Raster Maps

Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.

Ching-Chien ChenEmail:

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Yao-Yi Chiang   is currently a Ph.D. student at the University of Southern California (USC). He received his B.S. in Information Management from National Taiwan University in 2000 and then his M.S. degree in Computer Science from the USC in December 2004. His research interests are on the automatic fusion of geographical data. He has worked extensively on the problem of automatically utilize raster maps for understanding other geospatial sources and has wrote and co-authored several papers on automatically fusing map and imagery as well as automatic map processing. Prior to his doctoral study at USC, Yao-Yi worked as a Research Scientist for Information Sciences Institute and Geosemble Technologies. Craig A. Knoblock   is a Senior Project Leader at the Information Sciences Institute and a Research Professor in Computer Science at the USC. He is also the Chief Scientist for Geosemble Technologies, which is a USC spinoff company that is commercializing work on geospatial integration. He received his Ph.D. in Computer Science from Carnegie Mellon. His current research interests include information integration, automated planning, machine learning, and constraint reasoning and the application of these techniques to geospatial data integration. He is a Fellow of the American Association of Artificial Intelligence. Cyrus Shahabi   is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also a Research Area Director at the NSF’s Integrated Media Systems Center at the USC. He received his B.S. in Computer Engineering from Sharif University of Technology in 1989 and then his M.S. and Ph.D. degrees in Computer Science from the USC in May 1993 and August 1996, respectively. He has two books and more than hundred articles, book chapters, and conference papers in the areas of databases, geographic information system (GIS) and multimedia. Dr. Shahabi’s current research interests include Geospatial and Multidimensional Data Analysis, Peer-to-Peer Systems and Streaming Architectures. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems and on the editorial board of ACM Computers in Entertainment magazine. He is also a member of the steering committees of IEEE NetDB and the general co-chair of ACM GIS 2007. He serves on many conference program committees such as VLDB 2008, ACM SIGKDD 2006 to 2008, IEEE ICDE 2006 and 2008, SSTD 2005 and ACM SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early Career Awards for Scientists and Engineers. In 2001, he also received an award from the Okawa Foundations. Ching-Chien Chen   is the Director of Research and Development at Geosemble Technologies. He received his Ph.D. degree in Computer Science from the USC for a dissertation that presented novel approaches to automatically align road vector data, street maps and orthoimagery. His research interests are on the fusion of geographical data, such as imagery, vector data and raster maps with open source data. His current research activities include the automatic conflation of geospatial data, automatic processing of raster maps and design of GML-enabled and GIS-related web services. Dr. Chen has a number of publications on the topic of automatic conflation of geospatial data sources.