Automatic extraction of road intersections from raster maps

Chiang, Yao‐Yi; Knoblock, Craig A.; Chen, Ching-Chien

doi:10.1145/1097064.1097102

Cited by 33 publications

(42 citation statements)

References 9 publications

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“…To overcome these problems, we detect road intersections from maps that are preprocessed by a series of image processing techniques. In particular, as discussed in [13], our automated algorithm works as follows: (1) the algorithm analyzes maps to determine the road widths of double line maps in order to more accurately extract potential road segments, (2) it separates the linear structures (e.g., potential roads) from the maps by dynamically investigating thresholds and using the text/graphics separation techniques proposed in [5], (3) it uses morphological operators (including erosion and dilation operators) to reconnect and clarify the potential road segments, and (4) it detects corner points from the remaining lines, and it identifies a point as an intersection point if there are more than two road segments meeting at that point. On average, this algorithm can achieve 95% precision and 75% recall to detect map intersections.…”

Section: Identifying Intersection Points From Street Mapsmentioning

confidence: 99%

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Automatically and Accurately Conflating Raster Maps with Orthoimagery

Chen¹,

Knoblock

Shahabi

2007

Geoinformatica

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Recent growth of geospatial information online has made it possible to access various maps and orthoimagery. Conflating these maps and imagery can create images that combine the visual appeal of imagery with the attribution information from maps. The existing systems require human intervention to conflate maps with imagery. We present a novel approach that utilizes vector datasets as "glue" to automatically conflate street maps with imagery. First, our approach extracts road intersections from imagery and maps as control points. Then, it aligns the two point sets by computing the matched point pattern. Finally, it aligns maps with imagery based on the matched pattern. The experiments show that our approach can conflate various maps with imagery, such that in our experiments on TIGER-maps covering part of St. Louis county, MO, 85.2% of the conflated map roads are within 10.8 m from the actual roads compared to 51.7% for the original and georeferenced TIGER-map roads.

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Section: Identifying Intersection Points From Street Mapsmentioning

confidence: 99%

“…Neither map scale nor geocoordinates of ERSI maps are provided from this web site. 12 http://www.mapquest.com 13 http://maps.yahoo.com/ 14 http://terraserver-usa.com/ 15 MO-DOT is the road network data provided by the Missouri Department of Transportation. It is high quality vector data with highly accurate road geometry.…”

Section: (3) Vector Data (Road Network)mentioning

confidence: 99%

Automatically and Accurately Conflating Raster Maps with Orthoimagery

Chen¹,

Knoblock

Shahabi

2007

Geoinformatica

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“…see (Pouderoux et al, 2007), (Roy et al, 2007)). Other systems mostly consider the extraction of one or more types of features from complex maps ( (Chiang et al, 2005;Dhar & Chanda, 2006;Gamba & Mecocci, 1999;Kerle & Leeuw, 2009;Khotanzad & Zink, 2003;Leyk et al, 2006)), but do not address the problems of non-horizontal text or character recognition. Cao et al (Cao & Tan, 2002) used a commercial OCR to recognize text extracted from street maps that have limited intersection between different features.…”

Section: Document Image Analysis and Map Conversion Systems: A Brief Ovmentioning

confidence: 99%

Character Degradation Model and HMM Word Recognition System for Text Extracted from Maps

Pezeshk¹,

Tutwiler²

2011

Recent Advances in Document Recognition and Understanding

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“…We first identify road intersections on imagery by the technique described above, and then we also detect the road intersections on maps [8]. Finally, we apply geospatial point pattern matching algorithm to find matches between the two point sets.…”

Section: Road Network and Map Fusionmentioning

confidence: 99%

Geodec: Enabling Geospatial Decision Making

Shahabi

Chiang

Chung

et al. 2006

2006 IEEE International Conference on Multimedia and Expo

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The rapid increase in the availability of geospatial data has motivated the effort to seamlessly integrate this information into an information-rich and realistic 3D environment. However, heterogeneous data sources with varying degrees of consistency and accuracy pose a challenge to such efforts. We describe the Geospatial Decision Making (GeoDec) system, which accurately integrates satellite imagery, three-dimensional models, textures and video streams, road data, maps, point data and temporal data. The system also includes a glove-based user interface.

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Automatic extraction of road intersections from raster maps

Cited by 33 publications

References 9 publications

Automatically and Accurately Conflating Raster Maps with Orthoimagery

Automatically and Accurately Conflating Raster Maps with Orthoimagery

Character Degradation Model and HMM Word Recognition System for Text Extracted from Maps

Geodec: Enabling Geospatial Decision Making

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