{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83476"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83476","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Automatic Updating of Cartographic Databases","abstract":"To update a portion of an existing cartographic database, the common practice is to relate a new data file to an existing file by means of survey control points that are included in both files. The two digital maps may have different scales, different coordinate systems, and no identifying cartographic labels. In the absence of such survey control points, well-defined points in polygonal features such as building corners can be used. A prototype system was developed to perform matching of polygonal features and their vertices in vector data files. The system started with information extraction from polygonal features. Two algorithms based on Fourier descriptors and neural network approaches were developed for identification of common features. A sequence of validity checks combined with robust estimation provided a complete recognition of common features. Matching of individual vertex points was performed according to a similarity parameter, followed by a series of checks and validations. Matched features were then merged and updated with error analysis of error propagation. Experimental results have demonstrated the robustness and efficiency of the prototype system.","abstract_html":"To update a portion of an existing cartographic database, the common practice is to relate a new data file to an existing file by means of survey control points that are included in both files. The two digital maps may have different scales, different coordinate systems, and no identifying cartographic labels. In the absence of such survey control points, well-defined points in polygonal features such as building corners can be used. A prototype system was developed to perform matching of polygonal features and their vertices in vector data files. The system started with information extraction from polygonal features. Two algorithms based on Fourier descriptors and neural network approaches were developed for identification of common features. A sequence of validity checks combined with robust estimation provided a complete recognition of common features. Matching of individual vertex points was performed according to a similarity parameter, followed by a series of checks and validations. Matched features were then merged and updated with error analysis of error propagation. Experimental results have demonstrated the robustness and efficiency of the prototype system.","abstract_has_math":false,"creators":["Hsiao, Han-Wen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Wong, Kam W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:05:08Z","date_published":"2015-09-25T21:05:08Z","updated_at":"2026-07-22T22:26:21Z","subjects":["Geography"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9944884"],"render_values":[{"text":"(MiAaPQ)AAI9944884","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83476","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wong, Kam W."]},{"key":"dc:creator","label":"Author","values":["Hsiao, Han-Wen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:05:08Z","10000-01-01","1999"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Geography"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/83476","(MiAaPQ)AAI9944884"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["To update a portion of an existing cartographic database, the common practice is to relate a new data file to an existing file by means of survey control points that are included in both files. 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The two digital maps may have different scales, different coordinate systems, and no identifying cartographic labels. In the absence of such survey control points, well-defined points in polygonal features such as building corners can be used. A prototype system was developed to perform matching of polygonal features and their vertices in vector data files. The system started with information extraction from polygonal features. Two algorithms based on Fourier descriptors and neural network approaches were developed for identification of common features. A sequence of validity checks combined with robust estimation provided a complete recognition of common features. Matching of individual vertex points was performed according to a similarity parameter, followed by a series of checks and validations. Matched features were then merged and updated with error analysis of error propagation. 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