The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W3-2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-161-2022
https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-161-2022
10 Jan 2022
 | 10 Jan 2022

3D CITYGML BUILDING MODELS DEVELOPMENT WITH CROSS-SCALE QUERY DATABASE

H. Karim, A. Abdul Rahman, N. Z. Abdul Halim, G. Buyuksalih, and H. Rashidan

Keywords: Multi-scale, CityGML LoD, City Modelling, 3DCityDB, PostgreSQL Database, Quality Control, Scale Unique ID, Cross-scale Query

Abstract. CityGML model-based is now a norm for smart city or digital twin city development for better planning, management, risk-related modelling and other applications. CityGML comes with five levels of details (LoD, in version 2.0) of buildings. The LoDs are also known as pre-defined multi-scale models requiring a large storage-memory-graphic consumption than a single scale model. LoD CityGML models are primarily constructed using point cloud measurements and images of multiple systems, resulting in a range of accuracies and detailed model representations. Additionally, it entails several software, procedures, and formats for the construction of the respective LoDs prior to the final result in the CityGML schema. Thus, this paper discusses several issues of accuracy and consistency, proposing several quality controls (QC) for multiple data acquisition systems (e.g. airborne laser systems and mobile laser systems), model construction techniques (e.g. LoD1, LoD2, and LoD3), software (interchange formats), and migration to a PostgreSQL database. Additionally, the paper recommends the importance of minimising implementation errors. A scale-specific unique identifier is introduced to link all associated LoDs, enabling cross-LoD information queries within a database. Proper model construction, accuracy control, and format interchange of LoD models in accordance with national and international standards will undoubtedly encourage and expedite data sharing among data owners, agencies, stakeholders, and public users. A summary of the work and accomplishments is included, as well as a plan for future research on this subject.