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. Map generalization is not only affected by the factors such as map usage, scale, and feature distribution characteristics, but also affected by the level of understanding and skill of the mapper or computer. Under the same Constraint of indicator conditions, different mapper or computer use the same data but product different maps. Automatic map generalization is a kind of mapping technology with strong artificial intelligence, subjectivity and regionality. The necessary map comprehensive knowledge, experience of mapping and the correct comprehensive method of map are the key to the quality of the final map generalization product. This paper takes the automatic map generalization test of topographic data in a certain area as an example, starting from the quality requirements of 1:50 000 scale topographic data, discussing the selection, simplification, combination, smoothing, enhancement, displacement, exaggeration, aggregation of geographical features. It puts forward the models, contents, indicators and methods for quality assessment, and analyzes the typical quality problems after the generalization of features such as water system, habitation, road, landform, vegetation, soil and annotation. This paper also discusses the shortcomings and optimization measures of current automatic generalization in strategy, knowledge base and so on. The work of this paper can provide technical support for perfecting the theory and method of automatic map generalization, provide scheme reference for the real and reasonable assessment of topographic data automatic map generalization, and provide practical reference for multi-scale topographic map linkage update.

Quality Assessment of Topographic Data Automatic Map Generalization from Scale 1:10 000 to 1:50 000