Semantic enrichment of building and city information models: A ten-year review

Xue, Fan; Wu, Liupengfei; Lu, Weisheng

doi:10.1016/j.aei.2020.101245

Cited by 71 publications

(45 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The numerous works tackle the challenge of 3D reconstruction in contrast to the enrichment of existing 3D city models that gained little research attention (Xue et al, 2021). Nevertheless, adding geometric and non-geometric semantics is addressed e.g., by detecting and modeling windows on a façade based on the so-called voyeur effect (Tuttas and Stilla, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…The pipeline should be considered as an end-to-end solution in which different modules for geometry refinement and semantic enrichment can be added. While there are various definitions of semantic enrichment (Xue et al, 2021), we define it as a process of joining semantic information to a semantic city model both as a geometric and non-geometric semantic for application-specific tasks following the definition of (Xue et al, 2021). Whereas the geometry refinement refers to a challenge of the resolution increase of existing geometries for application-specific tasks abstracting from defined LoDs (Gröger et al, 2012) while maintaining existing geometric semantics (Xue et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…While there are various definitions of semantic enrichment (Xue et al, 2021), we define it as a process of joining semantic information to a semantic city model both as a geometric and non-geometric semantic for application-specific tasks following the definition of (Xue et al, 2021). Whereas the geometry refinement refers to a challenge of the resolution increase of existing geometries for application-specific tasks abstracting from defined LoDs (Gröger et al, 2012) while maintaining existing geometric semantics (Xue et al, 2021). Both concepts, however, are inline with 2.0 and 3.0 versions of CityGML modeling guidelines (Gröger et al, 2012;Kutzner et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

Wysocki

Schwab

Hoegner

et al. 2021

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

View full text Add to dashboard Cite

Abstract. Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these data types for nowadays’ applications, the fusion of both shall maximize their potential. Since testing and developing automated driving functions stands at the forefront of the challenges, we propose a pipeline fusing structured (CityGML and HD Map datasets) and unstructured datasets (MLS point clouds) to maximize their advantages in the automatic 3D road space models reconstruction domain. The pipeline is a parameterized end-to-end solution that integrates segmentation, reconstruction, and modeling tasks while ensuring geometric and semantic validity of models. Firstly, the segmentation of point clouds is supported by the transfer of semantics from a structured to an unstructured dataset. The distinction between horizontal- and vertical-like point cloud subsets enforces a further segmentation or an immediate refinement while only adequately depicted models by point clouds are allowed. Then, based on the classified and filtered point clouds the input 3D model geometries are refined. Building upon the refinement, the semantic enrichment of the 3D models is presented. The deployment of a simulation engine for automated driving research and a city model database tool underlines the versatility of possible application areas.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

Wysocki

Schwab

Hoegner

et al. 2021

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

View full text Add to dashboard Cite

show abstract

“…1enrichment) In case of too vague data, it could be possible to enrich semantics using several techniques, such as ontology-based inferences and machine learning techniques (e.g. Dou et al, 2015;Lüscher et al, 2007;Xue et al, 2021;Bloch, Sacks, 2018;Werbrouck et al, 2020). Attention should be paid for the vagueness value not to be affected by this processing.…”

Section: Harmonization Of the Semanticsmentioning

confidence: 99%

Multisource Spatial Data Integration for Use Cases Applications

Noardo¹

2021

Preprint

View full text Add to dashboard Cite

Big opportunities are given by the reuse and integration of data, which, nowadays, are more and more available, thanks to advances in acquisition and modelling technologies and the open data paradigm. Seamlessly integrating data from heterogenous data sources has been an interest of the geospatial community for long time. However, the higher semantic and geometrical complexity pose new challenges which have never been tackled in a comprehensive methodology. Building on the previous theories and studies, this paper proposes an overarching methodology for multisource spatial data integration. Starting from the definition of the use case-based requirements for the integrated data, it proposes a framework to analyse the involved datasets with respect to integrability and suggests actions to harmonise them towards the destination model. The overall workflow is explained, including the data merging phase and validation. The methodology is tested and exemplified on a case study. Considering the specific data sets’ features and parameters, this approach will allow the development of consistent, well documented and inclusive data integration workflows, for the sake of use cases processes automation and the production of Interoperable and Reusable data.

show abstract

“…The first is the introduction of information modeling technology (IMT) capital construction facilities (CCF) at all stages of the life cycle (LC), as well as information modeling of urban areas for the tasks of urban building and planning with a significant number of publications devoted to this problem [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A lifecycle management system for capital construction facilities based on a digital cloud platform implementing information modeling methods and technologies

Железнов

2021

E3S Web Conf.

View full text Add to dashboard Cite

The first is the introduction of information modeling technology (IMT) capital construction facilities (CCF) at all stages of the life cycle (LC), as well as information modeling of urban areas for the tasks of urban building and planning. The second is the digitalization of procedures and services in the field of construction, the formation of unified accessible data sets and information platforms that implement a unified information space for the construction industry. To implement the presented tasks, it is proposed to create a distributed life cycle control system for capital construction facilities (DCS LC CCF) and form a digital cloud platform (digital infrastructure) for the implementation of investment and construction projects based on the use of information modeling technologies, artificial intelligence and predictive analytics.

show abstract

Semantic enrichment of building and city information models: A ten-year review

Cited by 71 publications

References 57 publications

Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

Plastic Surgery for 3d City Models: A Pipeline for Automatic Geometry Refinement and Semantic Enrichment

Multisource Spatial Data Integration for Use Cases Applications

A lifecycle management system for capital construction facilities based on a digital cloud platform implementing information modeling methods and technologies

Contact Info

Product

Resources

About