GIS/LIS 1996, Denver, Colorado



    Automated Quality Control For GIS Data Conversion

    An Automatic Method for Georeferencing Scanned 1:24000 USGS Topographic Maps   

   A General Line-Following Algorithm for Raster Maps



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Automated Quality Control For GIS Data Conversion

Bishnu P. Phuyal, M.S. Robert W. Schmidley, Ph. D. The Ohio State University Center for Mapping

J. Raul Ramirez, Ph. D. Senior Research Scientist, Center for Mapping and Adjunct Assistant Professor, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University

The goal of the GISOM (Generating Information from Scanning Ohio Maps) project conducted by the Ohio State University Center for Mapping is to provide complete digital topographic data coverage for the State of Ohio. This work is being accomplished by the ongoing process of converting Ohio's 793 7.5 minute, 1:24,000-scale, USGS topographic quadrangles into digital form. As part of the GISOM research effort, quality inspection software has been developed to assist in the production of the DLG hypsography layer. This software functions by performing a pixel-by-pixel comparison of the scanned image of the source document against a rasterized version of the final DLG data. Contour line thickness is used as the basis for determining whether the DLG line approximates the centerline of the line in the scanned map with sufficient accuracy. A line in the DLG determined by the program to be inaccurate is flagged for subsequent inspection and correction by a human operator. The computerized quality inspection tool should prove to be more reliable, consistent, and efficient than human inspectors, thus reducing the overall cost of converting analog maps into digital data. 

































An Automatic Method for Georeferencing Scanned 1:24000 USGS Topographic Maps 

Anthony Githuku, M.S. Joseph Szakas, M.S. The Ohio State University Center for Mapping

J. Raul Ramirez, Ph. D. Senior Research Scientist, Center for Mapping and Adjunct Assistant Professor, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University

In the conversion of Analog to Digital Maps through the process of scanning and Head's-Up digitizing, one of the required tasks is to geo-reference the scanned raster map to the vector file for subsequent digitization. Four corresponding points (i.e. tic marks) in the raster and vector files provide the means of applying an eight parameter projective transformation to complete the geo-referencing process. And although automatic tic extraction from the vector file is a straight-forward process, automatic tic extraction from the raster file has many hurdles to overcome. Such hurdles include the inconsistent position of the tic marks, the shape and size of the tic marks due to the quality of the source material and scanning parameters, and the size of the raster file, for example. An implemented method is presented here that automates the tic extraction from the raster file for 1:24,000 scale USGS topographic maps. Reliability and consitency checks(metrics) are also discussed ensuring either a good 'warp' or an alert to the process manager.  



































A General Line-Following Algorithm for Raster Maps

Zhiyuan Zhao, The Ohio State University Center for Mapping

Alan Saalfeld, Ph.D. Assistant Professor, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University

J. Raul Ramirez, Ph. D. Senior Research Scientist, Center for Mapping and Adjunct Assistant Professor, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University

This paper presents a new algorithm for converting raster-scanned maps to vector format. By exploiting local geometric symmetries of multi-pixel width line features in raster format, the algorithm follows the center of a line feature by maintaining an equal distance to the exterior of the feature in the two directions perpendicular to the current moving direction. The algorithm generates vector data for this feature without thinning. Three goals are achieved for the algorithm: (1) it is suitable for general line features such as single lines, double lines (e.g. roads), irregular double lines (rivers), non-continuous lines of any pattern (streams, rail roads); (2) it can ignore most common noise found in scanned raster data; (3) it has high speed performance (running in time linearly proportional to the number of interior black pixels).

This work is a part of the joint project “Generating Information from Scanning Ohio Maps,” (GISOM), of the United States Geological Survey (USGS), five Ohio state agencies, and the Center for Mapping at the Ohio State University. An interactive implementation in a Microstation environment is replacing manual heads-up digitizing procedures.

For more information about this work and other works of the author check his homepage.


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