自动制图综合集技术、艺术与制图人员经验于一体,长期以来其自动化、智能化研究进展缓慢。基于机器学习的智能化自动综合也成为了制图综合发展过程中必须解决而仍未得到很好解决的核心难题之一。提出基于案例归纳学习的道路网智能选取方法,以制图专家道路网选取案例库为学习对象,以决策树算法为推理机,从专家案例库中自动归纳、推理来获取决策树,并转化为满足计算机自动执行的规则集,据此来进行道路网自动选取。从而解决了把难以形式化表达的制图专家经验自动转化为满足计算机自动综合要求的规则,并据此进行智能化自动综合这一难题。最后,采用实例对本文方法进行了验证,实验结果表明,本文方法能够从专家案例库中自动获取核心规则,并进行自动综合,综合结果能够有效地反映制图专家的制图综合经验,同时具有普适性,从而为智能化自动制图综合发展探索了新的途径。

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.