Improving the Consistency of Multi-LOD CityGML Datasets by Removing Redundancy

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Ledoux

Stoter

2016

ABSTRACT:The production and dissemination of semantic 3D city models is rapidly increasing benefiting a growing number of use cases. However, their availability in multiple LODs and in the CityGML format is still problematic in practice. This hinders applications and experiments where multi-LOD datasets are required as input, for instance, to determine the performance of different LODs in a spatial analysis. An alternative approach to obtain 3D city models is to generate them with procedural modelling, which is-as we discuss in this paperwell suited as a method to source multi-LOD datasets useful for a number of applications. However, procedural modelling has not yet been employed for this purpose. Therefore, we have developed RANDOM3DCITY, an experimental procedural modelling engine for generating synthetic datasets of buildings and other urban features. The engine is designed to produce models in CityGML and does so in multiple LODs. Besides the generation of multiple geometric LODs, we implement the realisation of multiple levels of spatiosemantic coherence, geometric reference variants, and indoor representations. As a result of their permutations, each building can be generated in 392 different CityGML representations, an unprecedented number of modelling variants of the same feature. The datasets produced by RANDOM3DCITY are suited for several applications, as we show in this paper with documented uses. The developed engine is available under an open-source licence at Github at http://github.com/tudelft3d/Random3Dcity.

“…This limitation, coupled with the general lack of multi-LOD data, renders multi-LOD CityGML data virtually non-existing in practice (Biljecki et al, 2015b).…”

Section: Citygmlmentioning

confidence: 99%

“…For testing software which identifies and links topological relationships between similar features across multiple LODs in CityGML (Biljecki et al, 2015b). The project focused on improving the consistency of multi-LOD datasets and their compression.…”

Section: Documented Applications Of Our Softwarementioning

confidence: 99%

Generation of Multi-Lod 3d City Models in Citygml With the Procedural Modelling Engine Random3dcity

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Ledoux

Stoter

2016

“…Simplified version of the CityGML Building module (yellow and green) with the LOD2+ extension (orange). Note that the surfaces can be reused in multiple storeys to increase consistency and reduce the storage footprint (Biljecki et al 2015).…”

Section: Automatic Generation Of Lod2+ Models From Lod2 Modelsmentioning

confidence: 99%

Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry

Boeters

Ohori

International Journal of Geographical Information Science

et al. 2015

Self Cite

The international standard CityGML defines five levels of detail (LODs) for 3D city models, but only the highest of these (LOD4) supports modelling the indoor geometry of a building, which must be acquired in correspondingly high detail and therefore at a high cost. Whereas simple 3D city models of the exterior of buildings (e.g. CityGML LOD2) can be generated largely automatically, and are thus now widely available and have a great variety of applications, similarly simple models containing their indoor geometries are rare.In this paper we present two contributions: (i) the definition of a level of detail LOD2+, which extends the CityGML LOD2 specification with indoor building geometries of comparable complexity to their exterior geometries in LOD2; and more importantly (ii) a method for automatically generating such indoor geometries based on existing CityGML LOD2 exterior geometries. We validate our method by generating LOD2+ models for a subset of the Rotterdam 3D data set and visually comparing these models to their real counterparts in building blueprints and imagery from Google Street View and Bing Maps. Furthermore, we use the LOD2+ models to compute the net internal area of each dwelling and validate our results by comparing these values to the ones registered in official government data sets.

“…Zhang et al [8] matches features by computing a compatibility coefficient, derived from the similarity in their geometry and that of their neighbors. Biljecki et al [7] detect identical geometries across multiple LODs of the same object in a CityGML file and reuse them to obtain a smaller file size.…”

Section: Identifying and Linking Corresponding Objectsmentioning

confidence: 99%

“…This means that complex relationships between objects, such as collapses, aggregations and others that are not one-to-one, are difficult to store, which causes, among others, update and maintenance problems, as well as inconsistencies [7]. It also complicates the storage of semantic information about these relationships.…”

Section: Introductionmentioning

confidence: 99%

Modeling a 3D City Model and Its Levels of Detail as a True 4D Model

Ohori

Ledoux

et al. 2015

IJGI

Self Cite

The various levels of detail (LODs) of a 3D city model are often stored independently, without links between the representations of the same object, causing inconsistencies, as well as update and maintenance problems. One solution to this problem is to model the LOD as an extra geometric dimension perpendicular to the three spatial ones, resulting in a true 4D model in which a single 4D object (a polychoron) represents a 3D polyhedral object (e.g., a building) at all of its LODs and a multiple-LOD 3D city model is modeled as a 4D cell complex. While such an approach has been discussed before at a conceptual level, our objective in this paper is to describe how it can be realized by appropriately linking existing 3D models of the same object at different LODs. We first present our general methodology to construct such a 4D model, which consists of three steps: (1) finding corresponding 0D-3D cells; (2) creating 1D-4D cells connecting them; and (3) constructing the 4D model. Because of the complex relationships between the objects in different LODs, the creation of the connecting cells can become difficult. We therefore describe four different alternatives to do this, and we discuss the advantages and disadvantages of each in terms of their feasibility in practice and the properties that the resulting 4D model has. We show how the different linking schemes result in objects with different characteristics in several use cases. We also show how our linking method works in practice by implementing the linking of matching cells to construct a 4D model.