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.@en

Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications.@en

We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planarity-sensible over-segmentation followed by semantic classification. The over-segmentation step generates an initial set of mesh segments that capture the planar and non-planar regions of urban scenes. In the subsequent classification step, we construct a graph that encodes the geometric and photometric features of the segments in its nodes and the multi-scale contextual features in its edges. The final semantic segmentation is obtained by classifying the segments using a graph convolutional network. Experiments and comparisons on two semantic urban mesh benchmarks demonstrate that our approach outperforms the state-of-the-art methods in terms of boundary quality, mean IoU (intersection over union), and generalization ability. We also introduce several new metrics for evaluating mesh over-segmentation methods dedicated to semantic segmentation, and our proposed over-segmentation approach outperforms state-of-the-art methods on all metrics. Our source code is available at https://github.com/WeixiaoGao/PSSNet.

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To keep the Panama Canal operational year round, the water level in the canal is maintained above the minimum depth. As water is passed through the locks and into the oceans on either side, the canals main reservoir looses fresh water, and this water is then replaced by the canals secondary reservoir which is Lake Alajuela. The Lake Alajuela reservoir is supplied of water by two large rivers, bringing in a sediment flux and decreasing the reservoirs capacity over time. Sedimentation sets a time limit to the use of the reservoir, and understanding this process and predicting the future morphological changes to occur in the reservoir are key to ensure continuation of operations in the Panama Canal. To predict the quantities of local sedimentation in the reservoir, a machine learning model is trained with features extracted from bathymetric models covering various years. These features are computed values that correlate with morphological processes. As such, the adaptiveness of a machine learning model along with the ability to extract morphological features from any elevation model provides the possibility to predict future sedimentation where other methods with empirical and numerical methods do not suffice due to a lack of data and restrictive parameters. Three types of machine learning algorithms and 21 features were initially tested for the purpose of the morphological modelling, during which the SVR model was the most successful. The testing was done on the R´ıo Chagres basin as well as the R´ıo Pequen´ı basin, both located in Lake Alajuela. Extensive hyperparameter tests were done to optimize and further test the performance of the SVR. Depending on the study area and the scale of the morphological behavior occurring, slightly different sets of features were most effective. Nevertheless, in both study areas tested the runoff model has proven to be a key factor for predicting the sedimentation and achieved a 70 to 80% accuracy for predicting zones of low or high sedimentation. The same model trained with the data from 1997 to 2018 was then used to predict 6 years into the future where more dynamic morphological behavior will occur according to the model. A downstream moving sediment front in the R´ıo Chagres basin is recognized and predicted to have moved 500 meters between the years 2018 and 2024. Such a sediment front travelling too far into the reservoir could have catastrophic consequences for all operations of the Panama Canal, making the awareness and anticipation of its progression highly important.

With the increasing demand for 3D city models in various applications, providing efficient access to 3D city models on the web has become very important and valuable. However, there is a lack of web services in which users can directly and effectively access the city objects contained in the 3D city models on the web. Another problem with web services related to 3D city models is that the size of 3D city models can become very large. As a consequence, the browser will not respond immediately until all city objects in the requested 3D city model have arrived. Addi-tionally, although file-based systems are the easiest way of data storage, they have deficiencies in web services that require scalability, efficiency, and flexibility in data access. To fix these is-sues, the goal of this research is to develop the an efficient way of disseminating 3D city models on the web, to discover the method of enabling 3D city models to be parsed incrementally and efficiently by the browser, and to explore a proper database solution for storing 3D city models to support the fast data access.

CityJSON has advantages in web applications over the CityGML data format, thus being chosen as the main datasets used in this research. Inspired by OGC API – Feature, a RESTful API for fast access to geospatial features in CityJSON has been developed. The second part of my research is related to the data streaming. CityJSONFeature has been proposed to enable CityJSON to be parsed incrementally on the web. To improve the overall data streaming perfor-mance, I proposed two methods to stream the data from the database to the RESTful API so that the RESTful API can immediately start constructing the first CityJSONFeature and sending it to the user. The third part of my research is to explore a proper database solution for CityJSON datasets to best support the fast data access in the RESTful API. In addition, some auxiliary data is added into the database to improve the RESTful API’s capabilities with efficient data filtering and streaming.

To evaluate the efficiency of the implemented methodology, a systematic benchmarking has been performed on three aspects: 1) the database schema, 2) the two streaming methods, and 3) the performance difference between the DBMS and the file system. The results show that in most use cases the DBMS can better support the RESTful API than the file system with the help of the built-in query and index mechanism. In addition, the overall streaming performance is largely improved when adding data streaming from the DBMS to the RESTful API. Lastly, based on the benchmarks, an optimal database schema has been chosen to support the RESTful API with fast data access, efficient data filtering and streaming.

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data. The contributions of this work are three-fold: (1) a new benchmark dataset of semantic urban meshes, (2) a novel semi-automatic annotation framework, and (3) an annotation tool for 3D meshes. In particular, our dataset covers about 4 km² in Helsinki (Finland), with six classes, and we estimate that we save about 600 h of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. We also compare the performance of several state-of-the-art 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source.

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The environmental pressure on the planet and its limited capacities has resulted in the need for new building methodologies that respect this limited capacity. Building methods based on the principles of the Circular Economy could pose an answer to this problem. However, limited knowledge and tools are available to bring this into practice. This research focuses on circular building from the perspective of re-using building components upfront and its relation with the project management of non-residential building projects in The Netherlands. The study aims to clarify the role of the project management and obtain more insight into the briefing, the project organization, and the selection of the project team, in relation to the re-use of building components. By conducting qualitative research, consisting of a literature study and empirical research, including multiple expert interviews with people involved in re-use projects and case study of a renovation and a newly built office project, a better understanding has been achieved. The research suggests that the management of the initiation phase is of great importance in setting a good starting point for re-use projects. The briefing could help in this by defining clear ambitions and goals. These can guide the team throughout the process. Besides, the way projects are organized could facilitate the inclusion of re-use. This includes the extension of the initiation phase, in which an inventory of reclaimed components is started. Throughout the process better integration between activities is needed to align the reclaimed components with the design, this comes with additional iteration. Lastly, the project team selection is found to have an impact on re-use. The composition of an interdisciplinary team is proposed to integrate planning and design activities and conduct them earlier in the process. This includes (new) collaborations with re-use experts. It is also found that the motivation of the team members could impact the amount of re-use. These findings are translated into a project management framework and in the visualization of the re-use process in a flow chart. These can be used by project managers in future re-use building projects.

The use of 3D models has been rapidly expanding, finding applications in both
scientific and commercial fields. One common requirement for these various applications is the geometrical and topological validity of these models. However, many models available online contain deficiencies in various forms, such as duplicated geometry, gaps in the surface, etc.. To cope with those deficiencies, a standard solution is the clean extraction of the model’s boundary, and simultaneously the model’s reconstruction in a way that its structure is valid. This thesis tackles a more generalized problem, the inside-outside classification for these models. Where many approaches might have requirements for running analysis, the methodology presented strives to robustly handle all cases.
These last decades, there have been various approaches in solving the ”inside -
outside classification problem”. A major attempt utilizes the winding number
algorithm, in order to assign values to elements whose position is relevant to the
input model. By assessing that value, a decision on whether the element in question is interior or exterior is taken. Other approaches work with casting rays, or other geometric analysis to also identify the borders of a model and segment the interior from the exterior. Also, since deficiencies inhibit the kick-starting of the necessary analysis, there are methods that try to restructure said models in order to clear any existing deficiencies. The methodology within this thesis will attempt a different approach from those that have been presented until now, which is transferring the problem from three into two dimensions. The first step is introducing a planar cross section on the area of interest. From there, through some graph reconstruction, geometric and optimization applications, a valid 1-manifold boundary of the cross-section is created. On that, the application of inside-outside classification through ray casting is possible. Assessing the results of the pipeline proves that the automated process can produce valid results, for a particular point of interest, related to an input model. The pipeline has been proven to function regardless of the cutting plane’s orientation, and can handle robustly a multitude of geometrically and topologically defective models. The results from this thesis can inspire further applications, and improvements on the pipeline can further evolve the quality of its outcome.

The relatively new Open Geospatial Consortium (OGC) standard LandInfra documents in its data model land and civil engineering infrastructure features. It has a Geography Markup Language (GML) implementation, OGC InfraGML, which has essentially no software support and is rarely used in practice. In order to share the benefits of LandInfra (and InfraGML) with a wider public, we have created the Infra Application Domain Extension (ADE), a CityGML ADE that allows us to store LandInfra features in CityGML. In this paper, we semantically map LandInfra to CityGML, describe our ADE, and discuss a few used cases where our ADE can be useful for applications for the built environment. We also provide software to automatically convert datasets from InfraGML to CityGML (and our ADE), and vice versa, as well as to validate them, which will help practitioners generate real-world InfraGML datasets.@en

It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the high-level and semantic aspects of GIS-BIM integration. This paper presents the results of the GeoBIM project, which tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models; (ii) the integration of existing GIS subsoil data in BIM; and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in The Netherlands. For each problem, this paper exposes in detail the issues faced, proposed solutions, and recommendations for a more successful integration.@en

Geographic information and building information modelling both model buildings and infrastructure, but the way in which they are modelled is usually complimentary and BIM-GIS integration is widely considered as a way forward for both domains. For one, more detailed BIM data can feed more general GIS data and GIS data can provide the context that is necessary to BIM data. While previous studies have focused on the theoretical aspects of such an integration at a schema level, in this paper we focus on explaining the geometric and topological issues we have found while trying to develop software to realise such an integration in practice and at a data level. In our preliminary results, which are presented here, we have found that many issues for such an integration remain: handling the geometric and topological problems in BIM models, dealing with bad georeferencing and figuring out the best way to convert data between IFC and CityGML are all open issues.@en

The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach.@en

This paper presents the generation of the 3D national building geo-data model of Turkey, which is compatible with the international OGC CityGML Encoding Standard. We prepare an ADE named CityGML-TRKBIS.BI that is produced by extending existing thematic modules of CityGML according to TRKBIS needs. All thematic data groups in TRKBIS geo-data model have been remodelled in order to generate the national large scale 3D geo-data model for Turkey. Specific attention has been paid to data groups that have different class structure according to related CityGML data themes such as building data model. Current 2D geo-information model for building data theme of Turkey (TRKBIS.BI) was established based on INSPIRE specifications for building (Core 2D and Extended 2D profiles), ISO/TC 211 standards and OGC web services. New version of TRKBIS.BI which is established according to semantic and geometric rules of CityGML will represent 2D-2.5D and 3D objects. After a short overview on generic approach, this paper describes extending CityGML building data theme according to TRKBIS.BI through several steps. First, building models of both standards were compared according to their data structure, classes and attributes. Second, CityGML building model was extended with respect to TRKBIS needs and CityGML-TRKBIS Building ADE was established in UML. This study provides new insights into 3D applications in Turkey. The generated 3D geo-data model for building thematic class will be used as a common exchange format that meets 2D, 2.5D and 3D implementation needs at national level. @en