Automatic alignment of vector data and orthoimagery for the national map
dg.o '06 Proceedings of the 2006 international conference on Digital government research
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
Augmenting cartographic resources for autonomous driving
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Conflation of road network and geo-referenced image using sparse matching
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Gaze map matching: mapping eye tracking data to geographic vector features
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Ortho-image analysis for producing lane-level highway maps
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Weighted multi-attribute matching of user-generated points of interest
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Recent growth of the geospatial information on the web has made it possible to easily access a wide variety of spatial data. The ability to combine various sets of geospatial data into a single composite dataset has been one of central issues of modern geographic information processing. By conflating diverse spatial datasets, one can support a rich set of queries that could have not been answered given any of these sets in isolation. However, automatically conflating geospatial data from different data sources remains a challenging task. This is because geospatial data obtained from various data sources may have different projections, different accuracy levels and different formats (e.g., raster or vector format), thus resulting in various positional inconsistencies. Most of the existing algorithms only deal with vector to vector data conflation or require human intervention to accomplish vector data to imagery conflation. In this paper, we describe a novel geospatial data fusion approach, named AMS-Conflation, which achieves automatic vector to imagery conflation. We describe an efficient technique to automatically generate control point pairs from the orthoimagery and vector data by exploiting the information from the vector data to perform localized image processing on the orthoimagery. We also evaluate a filtering technique to automatically eliminate inaccurate pairs from the generated control points. We show that these conflation techniques can automatically align the roads in orthoimagery, such that 75% of the conflated roads are within 3.6 meters from the real road axes compared to 35% for the original vector data for partial areas of the county of St. Louis, MO.