建筑物合并的Delaunay三角网分类过滤法

Classification and Filtering of Constrained Delaunay Triangulation for Automated Building Aggregation

建筑物面合并的方法是大比例尺地图综合研究的重要内容之一,本文提出了一种借助三角网进行建筑物合并的方法:针对约束Delaunay三角网中三角形的形态特征,提出了6种度量参数,依据这些参数进行排除和修复筛选操作;然后自动识别保留下来三角形的外轮廓作为建筑物之间的桥接部分,并对其进行直角化处理;接下来通过桥接部分和建筑物面的融合实现建筑物的合并,同时保持其直角化特征。最后,以多样性的实际数据进行实际处理,验证了该方法的正确性和高效性。

Building aggregation is an important part of research on large scale map generalization.A triangulation based approach is proposed from the perspective of shape features,six measure parameters of triangles in a constrained Delaunay triangulation are proposed.First of all,use the six measure parameters to determine which triangles are retained and which are erased.Then,the contours of retained triangles,as bridge areas between buildings,are automatically identified and right angle processed.And then,the buildings are aggregated with right angle feature retained by merging the bridge areas with connecting buildings.Finally,the approach is verified by being carried out on actual data.Experimental result shows that it is efficient and practical.