The intelligence of automated generalization developed slowly because of the integration of complex generalization technology

art

and cartographers' experience. Furthermore

the intelligent generalization based on machine-learning has also been one of the problems in the progress of automated generalization. A new approach of road network intelligent selection based on cases inductive reasoning is put forward in this paper

which takes the road network selection case lib of cartographers as leaning objects

the decision tree algorithm as reasoning machine

and concludes rules from expert case lib to form a decision tree. Then

the decision tree is transformed into rules that satisfy the computer's requirement. With these rules

computer could generalize road network selection automatically. Through this approach

the core problem of transforming cartographers' experience into rules that satisfying computer generalization automatically

and generalizing road network intelligently based on the rules is solved. Examples illustrate that

the new approach can conclude the core rules from the expert case lib and generalize map automatically

and the generalization results reflect the experts' experience of cartographic generalization effectively. Achieved generalization rules are suitable and usable to other special data of similar generalization conditions. Therefore

this method undertakes a new way for the intelligent automated generalization.