| 一种公寓式建筑物三维产权群集自动构建方法 |
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| 引用本文： | 史云飞,赵建青,李雪飞,王荣华,刘克辉,翟秋萍,田德.一种公寓式建筑物三维产权群集自动构建方法[J].武汉大学学报(信息科学版),2022,47(3):447-454. |
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| 作者姓名： | 史云飞 赵建青 李雪飞 王荣华 刘克辉 翟秋萍 田德 |
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| 作者单位： | 1.自然资源部城市国土资源监测与仿真重点实验室，广东 深圳，518034 |
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| 基金项目： | 自然资源部城市国土资源监测与仿真重点实验室开放基金KF-2018-03-034国家自然科学基金41601555山东省自然科学基金ZR2017BD018河北省地质资源环境监测与保护重点实验室开放课题JCYKT201910 |
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| 摘 要： | 建筑物在不同视角下分为物理群集和产权群集,后者依附于前者.现有的群集对象构建方法可以自动地构建同一栋建筑物的物理群集和产权群集,但生成的两个群集相互独立.这不仅增加建模成本,也不利于后期模型数据的更新和维护.针对该问题,研究公寓式建筑物物理群集与产权群集的关系,发现连通边界的层级性决定了胞腔聚合的产权体,提出了一种将物...
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| 关 键 词： | 公寓式建筑物三维模型 三维群集对象 节点关系图 拓扑数据模型 |
| 收稿时间： | 2020-03-11 |
A Method for Constructing Automatically 3D Property Right Cluster for Apartment Buildings |
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| Affiliation: | 1.Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China2.Hebei Key Laboratory of Geological Resources and Environment Monitoring and Protection, Shijiazhuang 050021, China3.School of Resources and Environment, Linyi University, Linyi 276000, China4.Linyi Land Survey and Planning Station, Linyi 276001, China |
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| Abstract: | Objectives Buildings can be divided into physical cluster and property right cluster from different perspectives, and the latter attached to the former. The existing methods can construct physical cluster and property right cluster of the same building automatically, but the two resulting clusters are independent of each other. This not only increases the cost of modeling, but also is not conducive to the update and maintenance of model data in the later period. Methods For the problem, we study the relationship between physical cluster and property right cluster of apartment buildings, and find that the hierarchy of connected boundaries determines the property right solids aggregated by cells, and present a method to transform physical clustering into property right clustering automatically. With existing physical clusters, the method transforms the cells of physical clusters into dual points, and the connected boundaries between cells into semantic edges, and the whole physical cluster into node relation graph with Poincaré duality transformation. A segmenting algorithm is designed for node relation graph, which can divide node relation graph into sub node relation graph representing proprietary and co-owned property according to the semantic information of the edges. Furthermore, the non-common boundary surfaces of the cell set corresponded by sub node relation graph are extracted to construct the property right solids, and the aggregation of the property right solids forms the property right cluster. Results Instead of building two separate clusters, the proposed method only builds a physical cluster and property right cluster is generated by the transformation. Conclusions The results show that the proposed method can identify the property right solids and construct property right cluster in the existing physical cluster automatically. It saves the modeling cost and facilitates the update and maintenance of the later data. |
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