Utilising urban context recognition and machine learning to improve the generalisation of buildings
Utilising urban context recognition and machine learning to improve the generalisation of buildings
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DOI：
10.1080/13658810902798099
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发表时间：
2010-02
影响因子：
5.7
通讯作者：
Stefan Steiniger;P. Taillandier;R. Weibel
中科院分区：
文献类型：
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作者：
Stefan Steiniger;P. Taillandier;R. Weibel
在地图生产系统中引入自动化综合程序要求综合系统能够在可接受的时间内处理大量地图数据，并且制图质量与传统地图产品相似。关于这些要求，我们研究两种互补的方法，应改善目前使用的国家地形测绘机构的综合系统。我们的重点特别是自我评估系统，以那些建立在多智能体范式上的系统为例。第一种方法旨在通过利用与空间背景有关的制图专家知识来提高制图质量。更具体地说，我们引入专家规则的基础上的建筑物分为五个城市结构类型，包括内城，城市，郊区，农村，工业和商业区的分类的泛化操作的选择。第二种方法旨在利用机器学习技术来提取地图，使我们能够减少搜索空间，从而缩短获得良好制图解决方案的时间。这两种方法分别进行测试，并结合从地图比例1：5000的目标地图比例1：25000的建筑物的概括。我们的实验表明，在效率和有效性方面的改进。我们提供的证据表明，这两种方法相互补充，专家和机器学习规则的组合比单独的方法得到更好的结果。这两种方法都足够通用，适用于其他形式的自我评估，基于约束的系统，而不是多智能体系统，也适用于其他要素类，而不是建筑物。问题已被确定为难以正式制图质量的控制的概括过程中的限制。
The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements, we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self‐evaluating systems, taking as an example those systems that build on the multi‐agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically, we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow us to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are tested individually and in combination for the generalisation of buildings from map scale 1:5000 to the target map scale of 1:25 000. Our experiments show improvements in terms of efficiency and effectiveness. We provide evidence that both approaches complement each other and that a combination of expert and machine learnt rules give better results than the individual approaches. Both approaches are sufficiently general to be applicable to other forms of self‐evaluating, constraint‐based systems than multi‐agent systems, and to other feature classes than buildings. Problems have been identified resulting from difficulties to formalise cartographic quality by means of constraints for the control of the generalisation process.