李安平, 翟仁健, 殷吉崇, 朱丽, 齐林君. 顾及空间结构关系的居民地自动合并方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20210731
引用本文: 李安平, 翟仁健, 殷吉崇, 朱丽, 齐林君. 顾及空间结构关系的居民地自动合并方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20210731
LI Anping, ZHAI Renjian, YIN Jichong, ZHU Li, QI Linjun. Automatic Aggregation of Building Considering the Spatial Structure[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20210731
Citation: LI Anping, ZHAI Renjian, YIN Jichong, ZHU Li, QI Linjun. Automatic Aggregation of Building Considering the Spatial Structure[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20210731

顾及空间结构关系的居民地自动合并方法

Automatic Aggregation of Building Considering the Spatial Structure

  • 摘要: 大比例尺的居民地合并是地图自动综合的主要内容之一,对地图编绘生产与空间数据多尺度表达具有重要意义。为保持合并前后居民地空间特征的一致性,本文提出一种顾及空间结构关系的居民地合并方法。首先构建距离自适应的约束Delaunay三角网,将邻近居民地之间的空间结构关系区分为6种,并根据桥接面类型划分为正桥接型和斜桥接型;然后重点针对正桥接型,通过定义邻近居民地之间的投影重叠线,判别和筛选桥接三角形,并对桥接部分进行直角化处理,使构建的桥接面与空间结构关系相适应。同时,提出邻近居民地之间的桥接距离计算方法,使居民地聚类符合制图要求和认知习惯;最后结合多组试验,验证了本文方法的有效性和普适性;通过对比试验,表明本文方法能更好地保持居民地的空间结构特征和直角性,并保证最小的合并面积。

     

    Abstract: Building aggregation is one of the main contents in map generalization at the micro scale, which is of great significance to map compilation and multi-scale representation of spatial data. In order to maintain the consistency of spatial characteristics, a building aggregation method considering the spatial structure is proposed. Firstly, based on distance-adaptive Delaunay triangles, the relationships of spatial structure between adjacent buildings are divided into six types. Meanwhile, these spatial structures are summarized as positive bridging type and oblique bridging type according to the bridging surfaces. Then, the bridging area is constructed according to bridging triangles, which are identified by projective overlap lines between adjacent buildings. The rectangular method is applied to the bridging area to maintain the spatial structure relationship. At the same time, the calculation of bridging distance between adjacent buildings is put forward for building clustering, which can meet the mapping requirements and cognitive habits. Finally, the effectiveness and universality of our method are verified by several experiments. Moreover, the comparative experiment shows that our method has advantage in maintaining the spatial structure characteristics and geometrical characteristics, including area consistency and rectangularity.

     

/

返回文章
返回