TOWARDS AUTOMATIC VALIDATION AND HEALING OF CITYGML MODELS FOR GEOMETRIC AND SEMANTIC CONSISTENCY

Author:

Alam N.,Wagner D.,Wewetzer M.,von Falkenhausen J.,Coors V.,Pries M.

Abstract

Abstract. A steadily growing number of application fields for large 3D city models have emerged in recent years. Like in many other domains, data quality is recognized as a key factor for successful business. Quality management is mandatory in the production chain nowadays. Automated domain-specific tools are widely used for validation of business-critical data but still common standards defining correct geometric modeling are not precise enough to define a sound base for data validation of 3D city models. Although the workflow for 3D city models is well-established from data acquisition to processing, analysis and visualization, quality management is not yet a standard during this workflow. Processing data sets with unclear specification leads to erroneous results and application defects. We show that this problem persists even if data are standard compliant. Validation results of real-world city models are presented to demonstrate the potential of the approach. A tool to repair the errors detected during the validation process is under development; first results are presented and discussed. The goal is to heal defects of the models automatically and export a corrected CityGML model.

Publisher

Copernicus GmbH

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Assessing and benchmarking 3D city models;International Journal of Geographical Information Science;2022-11-08

2. Techniques for improved CityGML models;Graphical Models;2019-11

3. Validation Methods of Geometric 3D-CityGML Data for Urban Wind Simulations;E3S Web of Conferences;2019

4. val3dity: validation of 3D GIS primitives according to the international standards;Open Geospatial Data, Software and Standards;2018-02-23

5. A continuous deployment-based approach for the collaborative creation, maintenance, testing and deployment of CityGML models;International Journal of Geographical Information Science;2017-10-26

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