Automatically identifying and georeferencing street maps on the web

Desai, Sneha S.; Knoblock, Craig A.; Chiang, Yao‐Yi; Desai, Kandarp; Chen, Ching-Chien

doi:10.1145/1096985.1096995

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…The result is a set of accurate features that can be used to exploit other geospatial data or to intergrate a raster map with other geospatial sources, thus creating an integrated view. In the work in [9], Desai et al applied our automatic intersection extraction technique on maps returned from image search engines and successfully identify the road intersection points for geospatial fusion systems to identify the geocoordinates of the input maps. Moreover, for a road extraction application, the georeferenced road intersections can be used as seed templates to extract the roads from imagery [13].…”

Section: Discussionmentioning

confidence: 99%

“…9 In addition, we deliberately selected 7 low-resolution abstract maps (resolutions range from 7 m/pixel to 14.5 m/pixel) to test our approach on more complex raster maps that have significant overlap between lines and characters. We first arbitrarily selected 70 detailed street maps with a resolution range from 1.85 m/pixel to 7 m/pixel.…”

Section: Methodsmentioning

confidence: 99%

“…We assume a more general scenario to handle various map sources [9]; and our approach requires no prior knowledge while it still can be tuned with additional information, if available. We assume a more general scenario to handle various map sources [9]; and our approach requires no prior knowledge while it still can be tuned with additional information, if available.…”

Section: Related Workmentioning

confidence: 99%

“…ESRI Map, MapQuest Map, TIGER/Line Map, Yahoo Map, A9 Map, MSN Map, Google Map, Map24 Map, ViaMichelin Map, Multimap Map, USGS Topographic Map, Thomas Brothers Map 9. Some of the sources do not provide resolution information 10.…”

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confidence: 99%

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Automatic and Accurate Extraction of Road Intersections from Raster Maps

et al. 2008

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Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as This work is based on an earlier paperroad orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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Automatic and Accurate Extraction of Road Intersections from Raster Maps

et al. 2008

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“…We retained only those images that did not exist in the repository. We then classified these images using our CBIR-based approach as well as using the method proposed in Desai et al 15 as a baseline comparison. The results of classification using both approaches are given in Table 1.…”

Section: Classifying Images Using a Cbir K-nearest-neighbor Methodsmentioning

confidence: 99%

A general approach to discovering, registering, and extracting features from raster maps

Knoblock

Chen²,

Chiang

et al. 2010

Document Recognition and Retrieval XVII

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Maps can be a great source of information for a given geographic region, but they can be difficult to find and even harder to process. A significant problem is that many interesting and useful maps are only available in raster format, and even worse many maps have been poorly scanned and they are often compressed with lossy compression algorithms. Furthermore, for many of these maps there is no meta data providing the geographic coordinates, scale, or projection. Previous research on map processing has developed techniques that typically work on maps from a single map source. In contrast, we have developed a general approach to finding and processing street maps. This includes techniques for discovering maps online, extracting geographic and textual features from maps, using the extracted features to determine the geographic coordinates of the maps, and aligning the maps with imagery. The resulting system can find, register, and extract a variety of features from raster maps, which can then be used for various applications, such as annotating satellite imagery, creating and updating maps, or constructing detailed gazetteers.

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