With the development of technologies and people’s raising standards for both indoor and outdoor environments, buildings have expanded their functionalities of merely being human’s everyday sheltering place. Common modern building applications include but are not limited to Building Energy Modelling (BEM), solar potential estimation, wind simulations, shadow analysis, noise propagation, and digital building permit checking. Among all
the common building applications, many of them only need to use the building’s envelope (A building’s envelope is composed of all building elements that are exposed to the outdoor environment). It is apparent that building envelope extraction can be beneficial to these applications. Currently, building envelopes are mainly constructed from point cloud data or satellite image data. With the increased usage of Building Information Model (BIM) models within the Architecture, Engineering and Construction (AEC) industry, more attempts have been done on extracting building envelopes from BIM models as well. In addition, the BIM-based building envelope tool can be used throughout the building’s life cycle. However, previously developed BIM-based building envelope extraction tools all have different types of flaws. The main problem with the existing methods is separating the building envelope elements is difficult and time-consuming. Considering the benefits extracting building envelopes from BIM models can bring and the limitations of the developed methods, it is beneficial to develop a different BIM-based building envelope extraction approach that can produce high-quality building envelopes efficiently. Therefore, in this study, to avoid the difficult problem of separating building interiors and building exteriors, we first extract the point cloud from BIM models and then extract this point cloud’s exterior boundary using the 3D alpha shape algorithm, and therefore obtain the building envelope. The quality of the extracted building envelopes is evaluated by their geometric accuracy, simplicity, and time efficiency. Among them, the geometric accuracy is evaluated by the deviations between the extracted surfaces and the original surfaces. The simplicity is measured by the number of vertices and faces, and the time efficiency is measured by the building envelope extraction speed. The results show that despite the developed method has some limitations, it can extract building envelopes with high geometric accuracy from various types of small-scale BIM models. The main limitations include its inability to process big BIM models, the extracted building envelope merely contains its geometry without the enrichment of topological and semantic information. For all the tested BIM models, the average geometric accuracy is within 2.00cm. Walls and roofs are very accurately extracted, with errors of less than 0.01cm. Windows, doors, and other types of small-scale objects are extracted with a bigger error of
0.1m to 0.3m. The extracted building envelope also simplifies the input models significantly regarding the number of vertices and the number of faces. Lastly, for small-scale models, the developed building envelope extraction tool is able to process them within 150s.

A 3D city model uses three-dimensional geometries to represent and model urban environments, in which the building model is the key feature. With the development of computer and data collection technologies, 3D city models are gaining growing capacities regarding storing rich information. This makes 3D city models more potentially useful than ever in the urban application domain.

When implementing 3D city models, the CityGML model is currently the most frequently used standard. It is being used by cities all over the world. CityJSON is an encoding for a subset of the OGC CityGML data model. It is a JSON-based data exchange format for digital 3D models of cities and landscapes, and it is easy for various operations. Due to the uniqueness of its structure, it is necessary to design corresponding data models to store CityJSON files in the database, to make querying and updating data within the database easy and convenient.

There are already some open-source solutions for storing CityJSON files in the database, 3DCityDB is one of them. 3DCityDB can store, manage and visualize data well, and it is open source. But the database design is very complex: for a tile of 3D BAG data, 3DcityDB uses a total of 66 tables to store data. The structure of the data model results in difficulties for database users to understand the imported data, and potentially leads to non-optimal operation performance when retrieving data for urban applications. Based on the drawbacks of the existing DBMS when dealing with the CityJSON data format, this project aims to develop a Postgres data model that can store CityJSON files simply and efficiently. The developed Postgres data model (CJDB) has a simpler table structure and data model design, a CityJSON data importer, and an interactive API user interface.

After going through this document (data model, importer and API section), the potential users will have the ability to:

• Import CityJSON files into a Postgres database,
• Perform queries on imported data,
• Perform operations using CJDB API.

In addition, by reading the benchmarking section, the users can gain an overview of the CJDB’s performance over the 3DcityDB’s.

The CJDB project is open-sourced, available on GitHub page.

The CJDB project will be potentially further developed by 3D geoinformation group of TU Delft and 3DGI.