3D Geoinformation Science

The CityGML standard enables the modelling of some topological relationships, and the representation in multiple levels of detail (LODs). However, both concepts are rarely utilised in reality. In this paper we investigate the linking of corresponding geometric features across multiple representations. We describe the possible topological cases, show how to detect these relationships, and how to store them explicitly. A software prototype has been implemented to detect matching features within and across LODs, and to automatically link them by establishing explicit topological relationships (with XLink). The experiments ran on our test datasets show a considerable number of matched geometries. Further, this method doubles as a lossless data compression method, considering that the storage footprint in the consolidated datasets has been reduced from their dissociated counterparts.

Today, Building Information Modeling (BIM) is mainly used in architecture. Typically, a BIM model contains detailed geometric and semantic information for design evaluation, simulation, and construction of the building. If, as on the regional and city levels, more than one building is considered, the information content of detailed BIM models might be too high. For applications like noise simulation or emergency management, representing buildings as block models, reduced outer-shell models or simplified indoor models are more suitable. Such models are typically found in Geospatial Information System (GIS) applications. This paper describes a process for BIM building models to extract different generalized representations for buildings and building elements. As an example, the definitions for such representations are based on the LoD concept of CityGML.

Topology plays a central role in the modelling and management of n-d geo-information. However, hitherto the standardization of n-d topological data models is still at its beginning. A general model based on oriented hierarchical d-Generalised Maps is proposed to handle topology in object-oriented geo-database management systems. The implementation of the approach is demonstrated as module in DB4GeO, our service-oriented 3-d geo-database architecture. A 3-d tracks planning application example in the city of Munich is presented demonstrating the usage of the n-d topological model. Finally, an outlook on our ongoing 3-d geoinfo research in the Dubai region is given.

In this paper, we present recent results on coupling geographic, infrastructure, and building models to multi-resolution numerical simulations. In order to achieve this, a parallel data access framework with interfaces to all parts of the simulation pipeline such as pre-processing, numerical simulation, and post-processing has been developed. The applicability of the approach presented in this work is shown by simulating urban flooding including surface flow of a city, the pipe network interaction, and its consequences to individual buildings. While the real life city model including the drainage system has been provided by the authorities of the city of Munich and comprises an area of about 2 by 2 km with detailed topography and a complete set of approximately 3,000 buildings modelled on LOD 1 of CityGML, IFC-models are the initial starting points for generating octrees on the level of individual buildings. In order to investigate the effects of the drainage system collapsing due to a heavy rain scenario, a fully three-dimensional parallel free surface flow simulation is incorporated with the interaction of the one-dimensional pipe-network flow. The whole simulation is performed on three levels of resolution, each of which is discretised by a fast voxelisation algorithm to generate a computational grid for the CFD simulation.

In the next few years, 3D data is expected to be an intrinsic part of geospatial data. However, issues on 3D spatial data management are still in the research stage. One of the issues is performance deterioration during 3D data retrieval. Thus, a practical 3D index structure is required for efficient data constellation. Due to its reputation and simplicity, R-Tree has been received increasing attention for 3D geospatial database management. However, the transition of its structure from 2D to 3D had caused a serious overlapping among nodes. Overlapping nodes also occur during splitting operation of the overflown node N of M + 1 entry. Splitting operation is the most critical process of 3D R-Tree. The produced tree should satisfy the condition of minimal overlap and minimal volume coverage in addition with preserving a minimal tree height. Based on these concerns, in this paper, we proposed a crisp clustering algorithm for the construction of a 3D R-Tree. Several datasets are tested using the proposed method and the percentage of the overlapping parallelepipeds and volume coverage are computed and compared with the original R-Tree and other practical approaches. The experiments demonstrated in this research substantiated that the proposed crisp clustering is capable to preserve minimal overlap, coverage and tree height, which is advantageous for 3D geospatial data implementations. Another advantage of this approach is that the properties of this crisp clustering algorithm are analogous to the original R-Tree splitting procedure, which makes the implementation of this approach straightforward.

This chapter introduces a novel technique for post-disaster mapping and disaster scale estimation based on an integrated framework of SAR remote sensing and a 3D city model database, optical remote sensing imagery is used for validation purpose. SAR based urban damage detection is well established and has been used for many years. We have showed that how the existing 3D City Model can be updated with the information extracted from satellite remote sensing data. The third dimension will play a very crucial role in evacuation rout planning of damaged or affected buildings. In this study in the proposed–three-level (L₁, L₂, L₃)–model damage assessment information is integrated with the semantic knowledge of 3D city models in order to better organize the search and rescue operation. L₁ includes remotely sensed Synthetic Aperture Radar (SAR) space-borne data collection from the affected areas; L₂ includes a change detection process; and L₃ includes the estimation of the most affected building(s). Using this model, we show how the day-night image acquisition capability of a SAR sensor and semantic information from a 3D city model can be effectively used for post disaster mapping for rapid search and rescue operations. For L₁, the combination of very high resolution SAR data and a 3D city model in CityGML format is used. L₂ works under predefined conditions to detect the types of changes that have occurred. In L₃, an interval-valued fuzzy soft set theory method is proposed with which to estimate the scale of damage to the affected structures (buildings). In this study, we show the potential application of existing 3D city models (with semantic knowledge) in combination with SAR remote sensing for post disaster management activities, especially for search and rescue operations.

With the increasing importance and usage of virtual 3D city models, a suitable continuation process becomes necessary to ensure the sustainability of 3D city models. If a new model was gathered e.g. by processing from LIDAR data or new data acquisition—knowledge about the differences between the old and the new city model is beneficial. This would allow the versioning, update and historicization. In this contribution we will present a concept for the detection of changes between two CityGML instance documents to ensure sustainable continuation processes in the field of 3D city models. The proposed concept will be based on techniques of existing change detection algorithms and object detection algorithms from the field of image analysis. The detected changes will be saved in a well-defined format so that following applications such as database operations can be performed or can make use of them. Finally, we examine the performance of our proof-of-concept implementation.

Today, many cities have at their disposal a digital model useful in many applications such as decision making in urban planning. 3D data representing objects in the city such as land and buildings often comes from successive acquisition campaigns. Unfortunately, digital models of cities can have many versions of the same area. Having tools to detect changes becomes a necessity. It is then possible to highlight any differences between multiple versions of the same area in 3D. A second application can be related to the possibility of making a temporal representation by taking into account the detected changes. In this paper, we propose a set of tools to detect changes. The use case is done on buildings. Our method is based on CityGML and cadastre files. The output is a CityGML file containing a representation of the evolution over time of the objects in the city.

The use of open GIS standards offers a broad variety of potential, particularly in the field of data exchange, data storage, and interoperability. GML and CityGML are excellent examples for the ontological description of real world objects by means of an open standard whereas SensorML serves to describe measurements, sensors and measuring platforms. The use of such standards offers not only the possibility of using a common standardised language, but also the use of open service standards. The combination of spatial data and sensor standards in services and service-oriented architectures goes far beyond previous existing solutions on the market and provides a novel platform for monitoring structures. That in fact is far more than a simple data storage model. The methods and models presented in this contribution allow a direct integration of sensor data and its provision through an open standard language. In this case, all the intermediate steps at any time through an open service interface are addressed and may be made available and provided to different actors and stakeholders participating in a construction scenario. The great potential and the added value of such an information system is the permanent availability of measurement and object data and an associated integrated analysis of sensor data in combination with a finite element model (FEM). The automatic derivation of a finite element model from the 3D structure model, the visualisation of FEM, the provision of raw (measurement) data and sensor information for each time of measurement transform the platform into a universal tool in the field of structural monitoring. This contribution introduces the individual components, the standards used and the interaction between the components to an overall system.

The increased growth of location-based services and the ongoing work on 3D GIS applications along with the fact that people spend most of their time indoors lead to a strong motivation for support and development in indoor space applications. When we ask someone the way, it is noticeable that people give route descriptions in an abstract and symbolic manner referring to landmarks or signs. Moreover, we know that people comprehend and structure space in recurring imaginative patterns—image schemata—while moving through and interacting with their environment. However, existing indoor navigation frameworks as well as building models do not integrate these cognitive concepts since mechanisms for defining such required semantics have not been developed yet. In order to support a navigation scenario in indoor environments by integrating human wayfinding principles in route instructions, we (1) identified potential landmarks based on cognitive concepts; (2) evaluated them according to a set of requirements; and (3) established a thematic framework for enhancing existing building models with the needed semantics and requirements. This framework will help developers to add the required attributes to the correct semantic classes of CityGML, IFC, or KML models to provide suitable routing instructions in terms of in-house routing out of these models.

In recent years, an increasing number of route planning applications and services have been developed and were brought to the market for different specific use cases. Most of those products are based on the client-server framework that typically combines the database server with the application server for storing the relevant network model, performing the routing calculation, and sending the result back to the client. However, this client-server framework mostly restricts users from changing, modifying, or augmenting the network model with respect to their specific contextual routing requirements. This paper presents a new approach for context aware route planning through coupling of a multilevel cloud-based system architecture with the complex IndoorGML data model, which is an upcoming OGC standard for representing and exchanging semantics, geometry, and topology information of indoor 3D building models. The geometric and logical network model can be rapidly extracted from the IndoorGML data model within the framework of the Multilayered Space-Event Model (MLSEM). Unlike the classical two-tier client-server architecture, the proposed multilevel cloud-based system allows exporting and uploading the network model from a database server to the cloud services that serve as an intermediate system-level to make the exported network model modifiable over the Internet without altering the original data. All changes of the network model with respect to different specific events will be applied in real-time, and the corresponding route planning calculations can then be carried out at the client-side respectively.

We present a comparison of a particles distribution model using 3D parameters derived from a CityGML-based 3D city model with an already advanced but 2D-based Land Use Regression model. Particles, especially ultrafine particles have significant influence on the health status of the urban population. Next to emission by cars and others, its distribution is tightly coupled to the local wind field and, therefore, to urban morphology influencing this wind field. However, 3D city models, especially CityGML have been almost ignored when modelling urban particles distribution. We introduce 3D parameters derived from a CityGML-based 3D city model in an already tested Land Use Regression model and explore the benefits of 3D city models in the field of particles distribution modelling, especially, by minimizing the number of parameters entered to the model and the good results that it has shown and explore the enhancement by combining both models.

3D technologies are becoming mature and more and more organisations are investing in 3D models for their areas. For National Mapping and Cadastral Agencies (NMCAs), that have a long history in collating and maintaining countrywide 2D-datasets, the major challenge is how to best adopt a 2D base into 3D environment with an established process of continuous incremental update, and making best use of available resources. To identify best and common practices, as well as remaining research challenges, since 2013 ten NMCAs work together in the European Spatial Data Research (EuroSDR) 3D Special Interest Group. This paper reports about an inventory that has been made on the state-of-the art of 3D products and plans that these NMCAs have as well as the challenges they face. The 3D modelling of buildings is explored in more detail, since buildings are prominent features in 3D city and landscape models. In addition, together with road objects (traffic infrastructure), building objects change often and therefore these require efficient update processes.

3D point clouds represent an essential category of geodata used in a variety of geoinformation applications and systems. We present a novel, interactive out-of-core rendering technique for massive 3D point clouds based on a layered, multi-resolution kd-tree, whereby point-based rendering techniques are selected according to each point’s classification (e.g., vegetation, buildings, terrain). The classification-dependent rendering leads to an improved visual representation, enhances recognition of objects within 3D point cloud depictions, and facilitates visual filtering and highlighting. To interactively explore objects, structures, and relations represented by 3D point clouds, our technique provides efficient means for an instantaneous, ad hoc visualization compared to approaches that visualize 3D point clouds by deriving mesh-based 3D models. We have evaluated our approach for massive laser scan datasets of urban areas. The results show the scalability of the technique and how different configurations allow for designing task and domain-specific analysis and inspection tools.

This paper introduces and formalizes a frame of reference for projective relations in 3D space that can be used to model human visual perception. While in 2D space visibility information can be derived from the concept of collinearity (thus, as ternary relations), in 3D space it can be derived from coplanarity, which calls for quaternary relations. Yet, we can retain ternary relations by anchoring our frame to an ubiquitous reference element: a general sense of vertical direction that, on Earth, can be the expression of gravity force or, in other cases, of the asymmetries of an autonomous agent, either human or robotic, that is, its vertical axis. Based on these observations, the presented frame of reference can be used to model projective and visibility information as ternary relations. Granularity and complexity of the models can be adjusted: we present two differently detailed realizations and discuss possible applications in Geographic Information Systems.