Automatically and Accurately Conflating Raster Maps with Orthoimagery

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

Knoblock

2008

Self Cite

The road network is one of the most important types of information on raster maps. In particular, the set of road intersection templates, which consists of the road intersection positions, the road connectivities, and the road orientations, represents an abstraction of the road network and is more accurate and easier to extract than the extraction of the entire road network. To extract the road intersection templates from raster maps, the thinning operator is commonly used to find the basic structure of the road lines (i.e., to extract the skeletons of the lines). However, the thinning operator produces distorted lines near line intersections, especially at the T-shaped intersections. Therefore, the extracted position of the road intersection and the road orientations are not accurate. In this paper, we utilize our previous work on automatically extracting road intersection positions to identify the road lines that intersect at the intersections and then trace the road orientations and refine the positions of the road intersections. We compare the proposed approach with the usage of the thinning operator and show that our proposed approach extracts more accurate road intersection positions and road orientations than the previous approach.

Section: Map Repositories Like the University Of Texas Map Librarymentioning

confidence: 99%

Section: Map Repositories Like the University Of Texas Map Librarymentioning

confidence: 99%

Automatic extraction of road intersection position, connectivity, and orientations from raster maps

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

Knoblock

2008

Self Cite

“…In addition to the USGS maps, we extended our experiments to test our technique on processing commonly accessible scanned maps using three sets of 60 maps (2000x2000 pixels each) cropped from three different scanned maps (350dpi) covering Bagdad, Iraq. The three scanned maps were published from different publishers 2 and different legends were used in the maps. Moreover, since the original paper maps have been folded, there were folding lines on the paper maps that caused inevitable shadows and color differences on different areas of the scanned raster maps.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…For example, the scanned USGS topographic maps can be downloaded from the Microsoft Terraserver and many other information rich raster maps can be found in map repositories such as the University of Texas Map Library. 1 To utilize the information in the raster maps, in our previous work, we developed a technology to first identify the road intersection templates in the raster maps [4] and then match the set of road intersection templates with another set of road intersection templates from a georeferenced data set (e.g., vector data) [2] to identify the geocoordinates of the maps and align the maps with the georeferenced data. For the automatic road intersection extraction process, in [4], we employed a histogram analysis approach to extract the foreground pixels from the raster maps and utilized a text/graphics separation 1 http://www.lib.utexas.edu/maps/ algorithm [1] to extract the road lines.…”

Section: Introductionmentioning

confidence: 99%

A Method for Automatically Extracting Road Layers from Raster Maps

2009 10th International Conference on Document Analysis and Recognition

Knoblock

2009

Self Cite

To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult since raster maps often contain numerous colors due to the noise introduced during the processes of image compression and scanning. In this paper, we present an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map. We can then use the identified road colors to extract road pixels from the map. Our approach can be used on scanned and compressed maps that are otherwise difficult to process automatically and tedious to process manually. We tested our approach with 100 maps from a variety of sources, which include 90 scanned maps with various compression levels and 10 computer generated maps. We successfully identified the road colors and extracted the road pixels from all test maps with fewer than four user labels per map on average.

“…By converting the text labels in a raster map to machineeditable text, we can produce geospatial knowledge for understanding the map region while other geospatial data are not ready available. Moreover, we can register a raster map to other geospatial data (e.g., imagery) [3] and exploit the recognized text from the map for indexing and retrieval of the other geospatial data.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Recognizing Text Labels in Raster Maps

2010 20th International Conference on Pattern Recognition

2010

Text labels in raster maps provide valuable geospatial information by associating geographical names with geospatial locations. Although present commercial optical character recognition (OCR) products can achieve a high recognition rate on documents containing text lines of the same orientation, text recognition on raster maps is challenging due to the varying text orientations and the overlap of text labels. This paper presents a text recognition approach that focuses on locating individual text labels in the map and detecting their orientations to then leverage the horizontal text recognition capability of commercial OCR software. We show that our approach detects accurate string orientations and achieves 96.2% precision and 94.7% recall on character recognition and 80.6% precision and 84.1% recall on word recognition.