G.A.K. Arroyo Ohori
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Development of a Geo to BIM Converter
CityJSON Importer Plugin for Autodesk Revit
Towards Extending CityGML for Property Valuation
Property Valuation ADE
In this chapter, we start from the typical concepts from Geographic Information System (GIS): data representation, acquisition, querying and analysis. We follow with the transition from 2 to 3D GIS and describe open standards such as CityGML and CityJSON and recent advances on 3D geospatial simulations, computing and real-time GIS and Internet of Things (IoT). Then we discuss the discrepancies in information management and modelling with respect to Building Information Modelling (BIM) and the related open standard, Industry Foundation Classes (IFC). We highlight the difference between Cartesian engineering coordinate systems and geospatial coordinate reference systems, contrast the procedural geometry definitions of IFC with the explicit geometries of GIS and look at implementation mechanisms such as boundary representations and polyhedral surface models and describe the semantic Level of Detail used in CityGML. The section that follows describes relevant processes supporting integration such as georeferencing, conversion of formats using semantic and geometric approaches and linking of heterogeneous information. We also highlight interoperability challenges that stem from consistency and validity of data, by interpreting the results of a recent benchmark on interoperability of the most common involved data formats (CityGML and IFC). We close with a conclusion and perspectives on the future with case studies on geo-enabled building permit checking and geospatial artificial intelligence and machine learning.
Satellite-Derived Bathymetry (SDB) can be calculated using analytical or empirical approaches. Analytical approaches require several water properties and assumptions, which might not be known. Empirical approaches rely on the linear relationship between reflectances and in-situ depths, but the relationship may not be entirely linear due to bottom type variation, water column effect, and noise. Machine learning approaches have been used to address nonlinearity, but those treat pixels independently, while adjacent pixels are spatially correlated in depth. Convolutional Neural Networks (CNN) can detect this characteristic of the local connectivity. Therefore, this paper conducts a study of SDB using CNN and compares the accuracies between different areas and different amounts of training data, i.e., single and multi-temporal images. Furthermore, this paper discusses the accuracies of SDB when a pre-trained CNN model from one or a combination of multiple locations is applied to a new location. The results show that the accuracy of SDB using the CNN method outperforms existing works with other methods. Multi-temporal images enhance the variety in the training data and improve the CNN accuracy. SDB computation using the pre-trained model shows several limitations at particular depths or when water conditions differ.
To support building permit issuing with automatic digital tools, the reuse of models produced by designers would make the process quicker and more objective. However, current studies and pilots often leave a gap with respect to the models as actually provided by architects, having varying quality and content. In this study, rather than taking a top down approach, we started from the available data and made the necessary inferences, which gave the opportunity to tackle basic and common issues often preventing smooth automatic processing. Specific characteristics of the IFC models were outlined and a tool was developed to extract the necessary information from them to check representative regulations. While the case study is specific in location, regulations and input models, the type of issues encountered are a generally applicable example for automated code compliance checking. This represents a solid base for future works towards the automation of building permits issuing.
3D city models are frequently used to acquire and store energy-related information of buildings for energy applications. In this context, CityGML is the most common data model, and the Energy ADE, one of its most complex extensions, provides a systematic way of storing detailed energy-related data in XML format. Contrarily, even though CityGML's JSON-based encoding, CityJSON, has an extension mechanism, an energy-related CityJSON Extension is missing. This paper, therefore, presents the first results of the development of a CityJSON Energy Extension and space heating demand calculation is utilized as the use case. The simplified version of the Energy ADE, called the Energy ADE KIT profile, is used to create a semi-direct translation to the CityJSON Energy Extension. This Extension is then validated through the official validator of CityJSON and the use case, and improvements are made considering the validation results. The space heating demand is calculated according to the Dutch standard NTA 8800 for a subset of Rijssen-Holten in the Netherlands although the solar gains calculation requires further review. The results show that the final CityJSON Energy Extension provides full support for space heating demand calculations based on the NTA 8800 and eliminates the deep hierarchical structure of the Energy ADE. A comparison on CityJSON file sizes shows a 25.2 MB increase after the required input data is stored in a CityJSON + Energy Extension file, which is not significant considering the high amount of data stored in the file. Overall, this paper shows that the CityJSON Energy Extension could provide an easy-to-use alternative to the CityGML Energy ADE.
Level of Detail (LoD) is a well known concept in 3D city models, used to designate different geometric detail that can be used in different applications. Nevertheless, multi-LoD datasets are hard to maintain and manage because of their intrinsic complexity. Versioning is a solution that aids in the storage and management of big and complex dataset, with its main goal being to facilitate the tracking of changes and collaboration. In this paper, we investigate the effects of utilising versioning and, more specifically, the concept of branches as a way to manage the evolution of multi-LoD datasets. We propose a framework according to which every LoD is stored in its own branch and can be extracted and updated independently. We tested this framework on a tile from 3D BAG, a dataset of 3D buildings for the whole of the Netherlands containing four LoDs (namely, LoD0, LoD1.2, LoD1.3 and LoD2.2). Our results suggest that there are certain benefits from this solution, such as the efficient tracking of changes for individual LoDs and the ability to extract and update the model using one LoD at a time. Nevertheless, there is a lot of complexity added to the process as a set of rules needs to be enforced when managing the model.