A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Verification-Based Approach for Automated Text and Feature Extraction from Raster-Scanned Maps
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Text/Graphics Separation in Maps
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Automatically and accurately conflating orthoimagery and street maps
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Automatically identifying and georeferencing street maps on the web
Proceedings of the 2005 workshop on Geographic information retrieval
Automatically and accurately conflating road vector data, street maps and orthoimagery
Automatically and accurately conflating road vector data, street maps and orthoimagery
Automatically identifying and georeferencing street maps on the web
Proceedings of the 2005 workshop on Geographic information retrieval
Identifying Maps on the World Wide Web
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Automatic extraction of road intersection position, connectivity, and orientations from raster maps
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Vectorization of gridded urban land use data
Proceedings of the 2010 Workshop on Procedural Content Generation in Games
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Numerous raster maps are available on the Internet, but the geographic coordinates of the maps are often unknown. In order to determine the precise location of a raster map, we exploit the fact that the layout of the road intersections within a certain area can be used to determine the map's location. In this paper, we describe an approach to automatically extract road intersections from arbitrary raster maps. Identifying the road intersections is difficult because raster maps typically contain multiple layers that represent roads, buildings, symbols, street names, or even contour lines, and the road layer needs to be automatically separated from other layers before road intersections can be extracted. We combine a variety of image processing and graphics recognition methods to automatically eliminate the other layers and then extract the road intersection points. During the extraction process, we determine the intersection connectivity (i.e., number of roads that meet at an intersection) and the road orientations. This information helps in matching the extracted intersections with intersections from known sources (e.g., vector data or satellite imagery). For the problem of road intersection extraction, we applied the techniques to a set of 48 randomly selected raster maps from various sources and achieved over 90% precision with over 75% recall. These results are sufficient to automatically align raster maps with other geographic sources, which makes it possible to determine the precise coverage and scale of the raster maps.