A Multilevel Identification Approach to Structured Building Clusters

The traditional clustering method is inappropriate for the gradual merging process of buil-dings, especially for a large span between two scale datasets. In order to resolve this problem, this paper proposed a multilevel identification approach to structured building clusters through inserting series middle scale between initial scale and target scale. Based on the spatial cognition and Gestalt principles, the structures of building group are abstracted and summarized into five typical patterns, and an identification approach is presented for building groups based on compactness network diagram and five typical patterns. Firstly, the neighborhood relationship is captured and the compactness network diagram is constructed with Delaunay triangulation. And the strongly compact loops, weakly compact loops and extended lines are generated for detecting the typical patterns. Then, the middle scale dataset can be obtained under the given constraints and thresholds, so that to achieve continuous visualization of multi-scale spatial data. Finally, experiments show that the identified result reflect the spatial distribution characteristics of buildings more clearly and seems more consistent with the habit of human cognition.