Conference Agenda

Findable Accessible Interoperable Reusable data are essential to support the ambitious goals of distributed data environments, digital twins, artificial intelligence and so on. Standards are critical to enable them. However, the need of developing very comprehensive standard for domain description often generates data models which are both over-specified (i.e. representing more than what is needed for the single use case) and under-specified (i.e. multiple implementation choices are allowed). Issues arise when this prevents the compliant datasets to be completely similar to each other, and may affect the trust the users have in standardised data and automatic processing. For this reason, the OGC Data Exchange Toolkit is developed, which is presented in this paper. It includes a standard data model profiling tool which exploits the semantic technologies, standing on the OGC Registry for Accessible Identifiers of Names and Basic Ontologies for the Web (Rainbow). The generated machine readable profile can effectively support data validation as well as the implementation of tools supporting the specific profile described. A second part of the toolkit is a human-friendly template intended to support data requirements definition and data modelling. An initial test of the developed toolkit is performed within the project 'Change toolkit for digital building permit' (CHEK), on the digitalisation of building permits.

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 use emph{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 ABCD has significantly lower demands in storage space (around a factor 10), allows for faster import of data, and its data retrieval speed is comparable (some queries are faster and some slower, we attempt to explain why).
The accompanying software (importer and exporter) is available under a permissive open-source license.

This paper introduces a new plug-in for QGIS that allows to connect to the free and open-source 3D City Database to load CityGML data, structured as classic GIS layers, into QGIS. The user is therefore not required to be a CityGML specialist, or a SQL expert, as the plug-in takes care of hiding from the user most of the complexity in terms of underlying data model and database schema implementation. The user can therefore load CityGML thematic “layers” (e.g. for buildings, bridges, vegetation, terrain, etc.), explore their geometries in 2D and 3D and access and edit the associated attributes. At the same time, depending on the user privileges, it is possible to delete features from the database using either normal QGIS editing tools, or a “bulk delete” tool, also included. The plug-in is composed of two parts, a server-side one, which must be installed in the 3D City Database instance, and the client-side one, which runs as a QGIS plug-in in strict sense. A GUI-based tool is also provided for database administrators in order to install/uninstall the database-side part of the plug-in, and manage users and their privileges. All in all, ”3DCityDB-Tools” plug-in facilitates the access to CityGML data for GIS practitioners from heterogeneous fields and expertise with the common denominator being the well-known QGIS environment.

Please refer to the attached file for the extended abstract.

Buildings are certainly the most important urban objects to represent in 3D city models due to their prominent role in the urban tissue and the amount of data available about them. This includes three-dimensional data from aerial Lidar. Lidar provides fast acquisition of abundant high-resolution topographical data, making it valuable for reconstructing detailed building models. However, in an urban context, buildings reconstruction using three-dimensional point clouds can be problematic due to several reasons. The process presents challenges not only in creating detailed and accurate models but also in the processing and computational requirements needed to generate these models. The processing of massive Lidar data is time consuming due to the huge amount of records and the iterative calculations. In this paper, we propose an approach for efficient and scalable 3D generation of large-scale detailed building models from aerial Lidar data. Our method involves data tiling and indexing for enhanced efficiency, along with parallel processing that accelerates the 3D reconstruction process by nearly five times. This overcomes limitations of existing methods that are computationally expensive and lack scalability, especially with large datasets. Additionally, we correct height inaccuracies in the final model to ensure precise and accurate results, leading to reliable applications.