GS
G. Stavropoulou
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1
We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based on CityJSON and JSON Text Sequences. CityJSONSeq was added to the CityJSON specifications version 2.0 to allow us to stream very large 3D city models. The main idea is to decompose a CityJSON dataset into its individual city objects (each building, each tree, etc.) and create several independent JSON objects of a newly defined type: CityJSONFeature. We elaborate on the engineering decisions that were taken to develop CityJSONSeq, we present the open-source software we have developed to convert to and from CityJSONSeq, and we discuss different aspects of the new format, eg filesize, usability, memory footprint, etc. For several use-cases, we consider CityJSONSeq to be a better format than CityJSON because: (1) once serialised it is about 10% more compact; (2) it takes an order of magnitude less time to process; and (3) it uses significantly less memory.
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We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based on CityJSON and JSON Text Sequences. CityJSONSeq was added to the CityJSON specifications version 2.0 to allow us to stream very large 3D city models. The main idea is to decompose a CityJSON dataset into its individual city objects (each building, each tree, etc.) and create several independent JSON objects of a newly defined type: CityJSONFeature. We elaborate on the engineering decisions that were taken to develop CityJSONSeq, we present the open-source software we have developed to convert to and from CityJSONSeq, and we discuss different aspects of the new format, eg filesize, usability, memory footprint, etc. For several use-cases, we consider CityJSONSeq to be a better format than CityJSON because: (1) once serialised it is about 10% more compact; (2) it takes an order of magnitude less time to process; and (3) it uses significantly less memory.
cjdb
A Simple, Fast, and Lean Database Solution for the CityGML Data Model
Conference paper
(2024)
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Leon Powałka, Chris Poon, Yitong Xia, Siebren Meines, Lan Yan, Yuduan Cai, Gina Stavropoulou, Balázs Dukai, Hugo Ledoux
When it comes to storing 3D city models in a database, the implementation of the CityGML data model can be quite demanding and often results in complicated schemas. As an example, 3DCityDB, a widely used solution, depends on a schema having 66 tables, mapping closely the CityGML architecture. In this paper, we propose an alternative (called ‘cjdb’) for storing CityGML models efficiently in PostgreSQL with a much simpler table structure and data model design (only 3 tables are necessary). This is achieved by storing the attributes and geometries of the objects directly in JSON. In the case of the geometries we thus adopt the Simple Feature paradigm and we use the structure of CityJSON. We compare our solution against 3DCityDB with large real-world 3D city models, and we find that cjdb has significantly lower demands in storage space (around a factor of 10), allows for faster import/export of data, and has a comparable data retrieval speed with some queries being faster and some slower. The accompanying software (importer and exporter) is available at https://github.com/cityjson/cjdb/ under a permissive open-source license.
...
When it comes to storing 3D city models in a database, the implementation of the CityGML data model can be quite demanding and often results in complicated schemas. As an example, 3DCityDB, a widely used solution, depends on a schema having 66 tables, mapping closely the CityGML architecture. In this paper, we propose an alternative (called ‘cjdb’) for storing CityGML models efficiently in PostgreSQL with a much simpler table structure and data model design (only 3 tables are necessary). This is achieved by storing the attributes and geometries of the objects directly in JSON. In the case of the geometries we thus adopt the Simple Feature paradigm and we use the structure of CityJSON. We compare our solution against 3DCityDB with large real-world 3D city models, and we find that cjdb has significantly lower demands in storage space (around a factor of 10), allows for faster import/export of data, and has a comparable data retrieval speed with some queries being faster and some slower. The accompanying software (importer and exporter) is available at https://github.com/cityjson/cjdb/ under a permissive open-source license.