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Data and information quality

A system for the automatic acquisition of land register maps is described. The system converts paper-based documents into digital form for integration into an existing database. Processing a map begins with its digitization by a scanning device. A key step in the system is the conversion from raster format to graph representation, a special binary image format suitable for processing line structures. Subsequent steps include vectorization of the line structures and recognition of the symbols interspersed in the drawing. The system requires operator interaction only to resolve ambiguities and correct errors in the automatic processes. The final result is a set of descriptors for all detected map entities, which is then stored in a database. It is shown that the image representation, vectorization techniques, and optical character recognition subsystem are quite general and that the methodology implemented in the system can be generalized to the acquisition of other classes of line drawings. >

An interpretation system for land register maps

This article is centred on analysing the state of the art of the conflation processes applied to geospatial databases (GDBs) from heterogeneous sources. The term conflation is used to describe the procedure for the integration of these different data, and conflation methods play an important role in systems for updating GDBs, derivation of new cartographic products, densification of digital elevation models, automatic features extraction and so on. In this article we define extensively each conflation process, its evaluation measures and its main application problems and present a classification of all conflation processes. Finally, we introduce a bibliography which the reader may find useful to further explore the field. It tries to serve as a starting point and direct the reader to characteristic research in this area.

Digital map conflation: a review of the process and a proposal for classification

Maris: map recognition input system

Recent growth of the geospatial information on the web has made it possible to easily access various maps and orthoimagery. By integrating these maps and imagery, we can create intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in diverse maps. However, accurately integrating maps and imagery from different data sources remains a challenging task. This is because spatial data obtained from various data sources may have different projections and different accuracy levels. Most of the existing algorithms only deal with vector to vector spatial data integration or require human intervention to accomplish imagery to map conflation. In this paper, we describe an information integration approach that utilizes common vector datasets as "glue" to automatically conflate imagery with street maps. We present efficient techniques to automatically extract road intersections from imagery and maps as control points. We also describe a specialized point pattern matching algorithm to align the two point sets and conflation techniques to align the imagery with maps. We show that these automatic conflation techniques can automatically and accurately align maps with images of the same area. In particular, using the approach described in this paper, our system automatically aligns a set of TIGER maps for an area in El Segundo, CA to the corresponding orthoimagery with an average error of 8.35 meters per pixel. This is a significant improvement considering that simply combining the TIGER maps with the corresponding imagery based on geographic coordinates provided by the sources results in error of 27 meters per pixel.

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Automatically and accurately conflating orthoimagery and street maps

A vision based method to automate map processing

The authors present an automated method for the digitization, vectorization and storage of land record maps. Map data is input into a vision system as raw data using a solid-state camera. After noise removal, the processed data is converted to vector data by an efficient algorithm. This point data that presents the key points can be stored on peripheral storage devices for future reconstruction and modification of the map. >

E. Ramanathan

Papers

A vision based method to automate map processing

Map processing methods: an automated alternative