The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation and Analysis of Monocular Building Extraction From Aerial Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Automatically and accurately conflating orthoimagery and street maps
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Automatic alignment of large-scale aerial rasters to road-maps
Proceedings of the 15th annual ACM international symposium 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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Oblique images are aerial photographs taken at oblique angles to the earth's surface. Projections of vector and other geospatial data in these images depend on camera parameters, positions of the entities, surface terrain, and visibility. This paper presents a robust and scalable algorithm to detect inconsistencies in vector data using oblique images. The algorithm uses image descriptors to encode the local appearance of a geospatial entity in images. These image descriptors combine color, pixel-intensity gradients, texture, and steerable filter responses. A Support Vector Machine classifier is trained to detect image descriptors that are not consistent with underlying vector data, digital elevation maps, building models, and camera parameters. In this paper, we train the classifier on visible road segments and non-road data. Thereafter, the trained classifier detects inconsistencies in vectors, which include both occluded and misaligned road segments. The consistent road segments validate our vector, DEM, and 3-D model data for those areas while inconsistent segments point out errors. We further show that a search for descriptors that are consistent with visible road segments in the neighborhood of a misaligned road yields the desired road alignment that is consistent with pixels in the image.