FORMALIZATION AND DATA ENRICHMENT FOR AUTOMATED EVALUATION OF BUILDING PATTERN PRESERVATION

Automated evaluation of generalization output relies to a large extent on that requirements (e.g. specifications, constraints) being formalized in machine-readable formats. Previous studies suggest that the formalization and automated evaluation are relatively easier for legibility constraints (improve the readability of maps) than for preservation constraints (preserving important real-world phenomena). Three major difficulties, i.e., pattern classification and characterization, pattern matching, and constraint formalization, in the automated evaluation of building pattern preservation constraint are analyzed in this paper. A classification of available building patterns is reviewed based on a previous work. In addition, the transition events describing allowed changes for building patterns to preserve during generalization are obtained through the study of existing maps series (from 1:10k to 1:100k). Based on the obtained knowledge on pattern types and acceptable transition events, an approach to automatically match corresponding building patterns at different scales is presented. The methodology proposed is validated by applying it to the interactively generalized data. The result shows promising results and also further improvement in order to apply the method in an overall evaluation to indicate acceptable generalization solutions

Using Surrogate Road Network for Map-Matching: A Sensitivity Analysis of Positional Accuracy

International audienc

Automated processing for map generalization with web services

In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequences