Geospatial Database for the Generation of Multidimensional Virtual City Models Dedicated to Urban Analysis and Decision-Making

Fricke, Andreas; Asche, Hartmut

doi:10.1007/978-3-030-24302-9_52

Cited by 6 publications

(6 citation statements)

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“…This is a result of an ongoing complex geopolitical situation in which Israel has annexed East Jerusalem to its territory contrary to international law. Crowd-sourced geodata, such as the OSM database, cannot compensate for the lack of official survey data, since the OSM system does not enforce a strict, authoritative geometric and semantic quality control of the data largely gathered by non-specialist volunteers [ 4 , 5 ]. In addition, the cityscape of Jerusalem is exposed to very high dynamics in the built-up area and infrastructure development.…”

Section: Use Case: the Complex Cityscape Of Jerusalemmentioning

confidence: 99%

“…In addition, the requirements point clouds need to comply with in order to perform the process in multidimensional point clouds, are checked during pre-processing [ 16 ]. The solution approach outlined above is developed within the scope of a wider R+D effort that deals with the with the creation, (re-)construction, administration and maintenance of virtual 3D city models in constantly changing urban environments [ 4 , 5 ]. An essential prerequisite for this work is the volume and quality of the geospatial source data.…”

Section: Approachmentioning

confidence: 99%

“…Although both schematisation and reconstruction are roughly based on the same principle, they differ in their handling of source data. This is apparent when it comes to the application of 3D building models which have massively increased in importance far beyond simple visualisations in times of so-called digital twins [ 5 ]. One driving factor behind this development is the rapidly growing availability of high-resolution input data.…”

Section: Approachmentioning

confidence: 99%

“…It has been pointed out that the availability and quality of source data is an essential prerequisite for the construction of a virtual 3D city model of the Jerusalem agglomeration. The ideal database would be a uniform data with identical spatial resolution and parametrisation that covers the entire study area [ 5 ]. As has been mentioned, such spatio-temporal data are not readily available, neither from topical official survey data nor from crowd-sourced OSM data or other regional data pools.…”

Section: Approachmentioning

confidence: 99%

“…This contrasts markedly with the constant dynamics of our natural environment. Hence the need to update geographical data has to be considered when it comes to processing the data for a topical or near real-time application [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Constructing Geo-Referenced Virtual City Models from Point Cloud Primitives

Fricke

Asche

2020

Computational Science and Its Applications – ICCSA 2020

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This paper presents a novel approach to construct spatially-referenced, multidimensional virtual city models from remotely generated point clouds for areas that lack reliable geographical reference data. A multidimensional point cloud is an unstructured array of single, irregular points in a spatial 3D coordinate system plus time stamp. If geospatial reference points are available, a point cloud is geo-referenced. Geo-referenced point clouds contain a high-precision reference dataset. Point clouds can be utilised in a variety of applications. They are particularly suitable for the representation of surfaces, structures, terrain and objects. Point clouds are used here to generate a virtual 3D city model representing the complex, granular cityscape of Jerusalem and its centre, the Old City. The generation of point clouds is based on two data acquisition methods: active data capture by laser scanning and passive data collection by photogrammetric methods. In our case, very high-resolution stereo imagery in visible light and near infrared bands have been systematically acquired an aerial flight campaign. The spatio-temporal data gathered necessitate further processing to extract the geographical reference and semantic features required in a specific resolution and scale. An insight is given into the processing of an unstructured point cloud to extract and classify the 3D urban fabric and reconstruct its objects. Eventually, customised, precise and up-to-date geographical datasets can be made available for a defined region at a defined resolution and scale.

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Section: Use Case: the Complex Cityscape Of Jerusalemmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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