语义辅助的CityGML模型一致性检测方法

doi:10.11947/j.AGCS.2021.20190474

测绘学报 ›› 2021, Vol. 50 ›› Issue (5): 664-674.doi: 10.11947/j.AGCS.2021.20190474

语义辅助的CityGML模型一致性检测方法

王永君^1,2,3, 陈青燕⁴, 杨玉娇^1,2,3, 陈学业⁵, 孙剑^1,2,3

1. 南京师范大学江苏省地理信息资源开发与应用协同创新中心, 江苏南京 210023;
2. 南京师范大学虚拟地理环境教育部重点实验室, 江苏南京 210023;
3. 南京师范大学地理环境演变国家重点实验室培育点, 江苏南京 210023;
4. 上海树融数据科技有限公司, 上海 200082;
5. 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

收稿日期:2019-11-13 修回日期:2020-08-30 发布日期:2021-06-03
通讯作者: 孙剑 E-mail:09332@njnu.edu.cn
作者简介:王永君(1973-),男,副教授,研究方向为多维空间数据集成与融合、激光点云数据处理、三维空间数据建模。E-mail:wangyongjun@njnu.edu.cn
基金资助:
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-2018-03-070）；国家自然科学基金（41771439）；国家重点研发计划（2016FB0502300）

Semantic-assisted CityGML model consistency checking method

WANG Yongjun^1,2,3, CHEN Qingyan⁴, YANG Yujiao^1,2,3, CHEN Xueye⁵, SUN Jian^1,2,3

1. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;
2. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
3. State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing Normal University, Nanjing 210023, China;
4. Shanghai Shurong Data Technology Co. Ltd., Shanghai 200082, China;
5. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China

Received:2019-11-13 Revised:2020-08-30 Published:2021-06-03
Supported by:
The Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation of Ministry of Natural Resources (No. KF-2018-03-070);The National Natural Science Foundation of China (No. 41771439);The National Key Research and Development Program of China (No. 2016YFB0502300)

摘要/Abstract

摘要： CityGML数据因建模方法、模型优化及数据转换等原因所导致的模型几何、拓扑、语义不一致性广泛存在，影响了对其的进一步应用。本文提出并构建了顾及语义约束的CityGML建筑物模型拓扑一致性规则集，设计了对CityGML LOD2/LOD3/LOD4多细节层次建筑物模型数据进行自动检测与修复的算法，并采用OGC官网公开数据对算法进行了验证试验。试验结果表明，所构建一致性规则集具有较好的完备性，算法能够检测出CityGML建筑物模型数据存在的绝大多数拓扑不一致问题，并能对其中部分的拓扑错误和几何错误进行自动修复。

关键词: CityGML, 建筑物模型, 语义规则, 拓扑一致性, 拓扑检测

Abstract: The inconsistency of model geometry, topology and semantics of CityGML data caused by the modeling method, model optimization and data conversion are widespread, which affects its further application. A rule set for CityGML building models was proposed and constructed in this paper which takes semantic constraints into account. Relevant algorithms for automatic detection and restoring of CityGML LOD2/LOD3/LOD4 multi-level-of-detail building model data are designed. Open data downloaded from OGC website was used to verify the rules and algorithms. Experimental results demonstrate that the constructed consistency rule set was complete and self-consistent, and proposed corresponding algorithms can detect most of the topology inconsistencies in CityGML building model data, and can automatically repair some of the topological and geometric errors.

Key words: CityGML, building model, semantic rules, topology consistency, topology validation

中图分类号:

P208

王永君, 陈青燕, 杨玉娇, 陈学业, 孙剑. 语义辅助的CityGML模型一致性检测方法[J]. 测绘学报, 2021, 50(5): 664-674.

WANG Yongjun, CHEN Qingyan, YANG Yujiao, CHEN Xueye, SUN Jian. Semantic-assisted CityGML model consistency checking method[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(5): 664-674.

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语义辅助的CityGML模型一致性检测方法

Semantic-assisted CityGML model consistency checking method

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