Automatic alignment of large-scale aerial rasters to road-maps

Wu, Xiaqing; Carceroni, R.L.; Fang, Hui; Zelinka, Steve; Kirmse, Andrew

doi:10.1145/1341012.1341035

Cited by 29 publications

(26 citation statements)

References 12 publications

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“…[18,7,16,1] propose image-based features for extracting roads, intersections, buildings and compound objects such as harbors from aerial imagery. However, such man-made objects have a strong prior in shapes and recurrent patterns, so the features cannot be directly applied to tree detection.…”

Section: Related Workmentioning

confidence: 99%

Tree detection from aerial imagery

Yang

Praun

et al. 2009

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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We propose an automatic approach to tree detection from aerial imagery. First a pixel-level classifier is trained to assign a {tree, non-tree} label to each pixel in an aerial image. The pixel-level classification is then refined by a partitioning algorithm to a clean image segmentation of tree and non-tree regions. Based on the refined segmentation results, we adopt template matching followed by greedy selection to locate individual tree crowns.As training a pixel-level classifier requires manual generation of ground-truth tree masks, we propose methods for automatic model and training data selection to minimize the manual work and scale the algorithm to the entire globe. We test the algorithm on thousands of production aerial images across different countries. We demonstrate high-quality tree detection results as well as good scalability of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Tree detection from aerial imagery

Yang

Praun

et al. 2009

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Self Cite

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“…For example, we do not assume that the transformation between the vector data and imagery is a simple translation or an affine transformation [8] [14]. In fact, the transformation between vector data and its projection in oblique imagery is non-linear.…”

Section: Related Workmentioning

confidence: 99%

Validation of vector data using oblique images

Mishra

Ofek

Kimchi

2008

Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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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.

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“…In general, a GIS [1][2][3] provides facilities for data capture, data management, data manipulation and analysis. Map is the recognition and representation of the location of geographic features on the landscapes of the Earth surface.…”

Section: Introductionmentioning

confidence: 99%

Digitization of Polygon Objects in a Raster Map based on the Direction

Kundu¹,

Halder²,

Mandal³

2017

IJCA

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This article represents the digitization of polygon objects from a black and white raster map. Based on the selection of direction mouse click operation is perform on the boundary of the polygon object or closer to the boundary. If the initial click point is not black then eight connected points of the click point are compared to obtain the nearest black boundary point and it is considered as starting point (i.e. sp point). After obtained the starting point (sp), eight connected points of the starting point (SP) are calculated and the appropriate black point from the eight connected points is selected according to the rules of the direction and this process is repeated until it reached to the starting point (SP). This proposed technique requires less computation with minimal memory requirement and boundary points are obtained in a single click. The boundary points are minimized approximately 52% through the computation of minimal set of boundary points of a segments. Comparisons with existing reveals that the proposed technique is outperform than existing.

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Automatic alignment of large-scale aerial rasters to road-maps

Cited by 29 publications

References 12 publications

Tree detection from aerial imagery

Tree detection from aerial imagery

Validation of vector data using oblique images

Digitization of Polygon Objects in a Raster Map based on the Direction

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