Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
Digital Image Processing
Optimal Motion and Structure Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Gibbs Point Process for Road Extraction from Remotely Sensed Images
International Journal of Computer Vision
Automatically and accurately conflating orthoimagery and street maps
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Generalized RANSAC Framework for Relaxed Correspondence Problems
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Validation of vector data using oblique images
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Experiences on Processing Spatial Data with MapReduce
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Tree detection from aerial imagery
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
Automatic registration of oblique aerial images with cadastral maps
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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This paper introduces a practical approach to register large-scale GIS imagery to a database of road vectors automatically. The proposed approach breaks the global alignment problem into a set of localized domains (tiles). Within each tile, the displacement between imagery and vectors is approximated by a translation. Finally, a global thin-plate-spline warp based on these local approximations is applied to register the imagery to the vector data. The critical step in this approach is a fully automatic algorithm to compute the best imagery-to-vectors translation within a tile. The proposed algorithm performs vector-guided extraction of road features, aggregates features obtained in the neighborhood of multiple vectors, and then estimates the best translation through a least-squares optimization applied to a selected subset of the aggregated features. It also computes a confidence value for each processed image tile, so that a human operator can easily find out the places where the automatic approach has encountered difficulties, if necessary. The algorithm has been tested on hundreds of production satellite images of different countries. It has correctly registered over 80 percent of the imagery, and consistently reported low confidence values for the rest.